IDENTIFICATION OF MAJOR MINERALS IN
IGNEOUS ROCK MICROSCOPIC IMAGES FROM
THIN SECTIONS THROUGH DEEP NEURAL
NETWORK ANALYSIS

Kouadio Krah, Sié Ouattara, Gbele Ouattara, Marc Euphrem Allialy, Alain Clement

► To cite this version:

Kouadio Krah, Sié Ouattara, Gbele Ouattara, Marc Euphrem Allialy, Alain Clement. IDENTIFICATION OF MAJOR MINERALS IN IGNEOUS ROCK MICROSCOPIC IMAGES FROM THIN SECTIONS THROUGH DEEP NEURAL NETWORK ANALYSIS. Artificial Intelligence in Geosciences, 2025, 6 (2), pp.100157. 10.1016/j.aiig.2025.100157. hal-05291745

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# Identification of major minerals in igneous rock microscopic images from thin sections through deep neural network analysis

Kouadio Krah<sup>a,b,*</sup>, Sié Ouattara<sup>b,c,d</sup>, Gbele Ouattara<sup>a,b</sup>, Marc Euphrem Allialy<sup>e,f</sup>, Alain Clement<sup>g</sup>

## ARTICLE INFO

### Keywords:

- Mineral identification

- Deep learning

- Multi-label classification

- Images

- CNN

## ABSTRACT

Several socio-environmental needs (medicine, industry, engineering, orogenesis, genesis, etc.) require minerals to be more precisely defined and characterised. The identification of minerals plays a crucial role for researchers and is becoming an essential aspect of geological analysis. However, traditional methods relied heavily on expert knowledge and specialised equipment, making them labour-intensive, costly and time-consuming. This dependence is often labour-intensive, not to mention costly and time-consuming. To address this issue, some researchers have opted for machine learning algorithms to quickly identify a single mineral in a microscopic image of rocks. However this approach does not correspond to patterns of mineral distribution, where minerals are typically found in associations. These associations make it difficult to accurately identify minerals using conventional machine learning algorithms. This paper introduces a deep neural learning model based on multi-label classification, utilizing the problem adaptation method to analyse microscopic images of rock thin sections. The model is based on the ResNet50 architecture, which is designed to analyse minerals and generates the probability of a mineral presence in an image. This method provides a solution to the dependence between associated minerals. Experiments on many test images showed a model confidence, achieving average precision, recall and F1_score 97.15 %, 96.25 % and 96.69 %, respectively. Visualisation of the class activation mapping using the Grad-CAM algorithm indicates that our model is likely to locate the identified minerals effectively. In this way, the importance of each pixel with the class of interest can be assessed using heat maps. The recorded results, in terms of both performance and pixel_level evaluation, demonstrate the promising potential of the model used. It can therefore be considered for multi-label image classification, particulary for images representing rock minerals. This approach serves as a valuable support tool for geological studies.

## 1. Introduction

Mineralogical observation consists of visualising the product of a complex natural process that can result in minerals with specific properties. The earliest studies of minerals and other so-called precious stones can be traced back to the writings of Aristote (384–322 BC) and

Theopharatus (370–285 BC) (Maćkowska, 2018). Later, Georgius Agricola (1494–1555), in his book ‘De natura Fossilium’, proposed the first mineralogical classification based on physical properties. These attempts led to classifications based on the chemical composition of minerals, their class, type, crystal structure group, and spatial arrangement, linked to the development of crystallography and

<footnote><sup>a</sup> Laboratoire des Sciences Géographiques, du Génie Civil et des Géosciences (LASCIG3) / Institut National Polytechnique HOUPHOUËT-BOIGNY (INP-HB), Côte d'Ivoire</footnote>

<footnote><sup>b</sup> Institut National Polytechnique HOUPHOUËT-BOIGNY (INP-HB), Ecole Doctorale Polytechnique (EDP), BP 1093, Yamoussoukro, Côte d'Ivoire</footnote>

<footnote><sup>c</sup> Laboratoire des Sciences et Technologies de la Communication et de l'Information (LSTCI), Institut National Polytechnique HOUPHOUËT-BOIGNY (INP-HB), BP:1093, Yamoussoukro, Côte d'Ivoire</footnote>

<footnote><sup>d</sup> Ecole de Géomatique et du Territoire (EGT), Abidjan, Côte d'Ivoire</footnote>

<footnote><sup>e</sup> Université Félix HOUPHOUËT-BOIGNY de Cocody-Abidjan Côte d'Ivoire (UFHB), Côte d'Ivoire</footnote>

<footnote><sup>f</sup> Unité de formation et de recherche (UFR) des sciences de la terre et des ressources minières, Côte d'Ivoire</footnote>

<footnote><sup>g</sup> LARIS, SFR MATHSTIC, Université d'Angers, Angers, France</footnote>

<footnote>Peer review under the responsibility of KeAi Communications Co., Ltd.</footnote>

<footnote>* Corresponding author. Institut National polytechnique de Yamoussoukro, BP 1093, Yamoussoukro, Côte d'Ivoire.
E-mail address: krah.kouadio21@inphb.ci (K. Krah).</footnote>
crystallochemistry (Dana, 1837). To this end, the most recent version, published in 2001 (Pring, 2003), classifies mineral phases into ten (10) common groups. While it is based on Dana's system, it features a different numbering scheme: Native elements, sulfides and sulfosalts, halides, oxides, carbonates and nitrates, borates, **sulfates**, phosphates, silicates divided into six (6) subclasses and organic minerals.

Since its invention, microscopy has consistently held an un-
disputed position as a fundamental analytical tool in petrography.
This led to the conventional classification of rocks through optical
observation, following the microscopic images produced. However, this
method requires expertise in mineralogy. Identifying minerals is an
important step in determining their practical applications. Thus, the
presence and relative abundance of minerals contribute to the naming,
dating, and geological interpretation of the origin of igneous rocks
(Gehrels, 2014). In petrography, for example, identifying the complete
mineral assemblage provides valuables insights into the conditions of
crystallization. Certain minerals, such as feldspars and pyroxenes, are
also recognised for their industrial significance (Geisler et al., 2003). For
this purpose, the most common image-based optical identification
method is performed by microscopic images (Izadi et al., 2013) and
ordinary photographs (Ramil et al., 2018; Zeng et al., 2021). Image
classification is the assignment of a label from a fixed set of classes to an
input image.

Over the last few decades, image classification has become one of the
most effective ways of distinguishing between different types of image
on the basis of the features extracted from them (Foody and Mathur,
2004; Zhang et al., 2018). The classification of rock images using min-
erals is an important prerequisite for recognising the characteristics of
rocks. It is much more automated, thanks to image processing
techniques.

Baykan and Yilmaz in 2010 identified minerals (quartz, muscovite,
biotite, chlorite and opaque minerals) with a high degree of accuracy
based on colour using an artificial neural network (ANN) (Baykan and
Yilmaz, 2010; Borazjani et al., 2016) used a multi-layer perceptron as a
texture identifier to extract textural attributes from carbonate rocks and
determine the proportion of each porosity type related to its extracted
features (Izadi et al., 2013). used a neural network (MLP) to identify
minerals based on their textural characteristics. Most of this work has
been carried out using single-label images. The data for training a
network contains clear, focused images, with a single element in the
image and no background noise or clutter. This was approach was
employed by Asiedu and his team, who developed the Minet model
using a convolutional neural network (CNN) to identify and characterise
7 classes of minerals using a set of images of minerals collected on the
Internet (Asiedu et al., 2021). However, these single-label classification
methods have proved highly effective. However, minerals found in
natural environments are mainly found in association with other min-
erals (Aligholi et al., 2017). This implies that a single mineral photo-
micrograph may contain several minerals. This takes into account the
crystallization history of the rock. Likewise, in igneous rocks, grain sizes
are often well differentiated. The aspects can facilitate the extraction of
features or patterns (such as shape and texture) by models or descriptors.
This highlights the significant influence of grains properties on model
performance. Recent studies have found that a real-life image can pre-
sents a variety of scenes, objects and actions in a single image. They
propose a Multi-Label Classification. This approach to classifying mul-
tiple object labels relies on individual classifiers for each label category
(Tsoumakas and Katakis, 2007). It includes three main families of
methods: problem transformation (Luaces et al., 2012; Tsoumakas et al.,
2009), problem adaptation and ensemble methods (Saini and Ghosh,
2017; Tsoumakas and Vlahavas, 2007). In a Multi-Label approach, it is
necessary to find discriminating features to identify each of them in the
given image which contraste certain applications where there may be
dependencies between target labels may exist. It uses a presence prob-
ability ranking for the final classification. This approach is highly suit-
able for rock mineral images, referred to as natural images due to the

challenges of performing segmentation. In this article, we aim to address
the issue of label **dependency**, which is particularly pronounced in the
case of minerals. To this end, we propose a problem adaptation
approach. This approach involves using correlation between labels to
better classify images of minerals with several labels. This method takes
advantage of label co-occurrences in a simple but effective way. The goal
is to train a deep learning model using available mineral data (Krah
et al., 2024), representing realistic data, i.e. data containing other ob-
jects in their natural environment. The model should be able to predict
the probability for each class of seven minerals (quartz, muscovite,
biotite, alkali feldspar, pyroxene, plagioclase and amphibole). Although
some authors, have classified minerals into series for example, zircon
(igneous, metamorphic, hydrothermal) using deep learning (Zheng
et al., 2022), our approach treats each mineral as a distinct entity, a
choice driven by the lack of open-source databases structured by series.
The study is based on microscopic images of magmatic rock samples,
using transfer learning with the ResNet50 model.

## 2. Conceptual background

A reliable classification based on microscopic images provides a
foundation for the development of structured databases. These data-
bases serve as archives that can be automatically exploited by geologists
or artificial intelligence systems for analysis, by practitioners to optimize
fields and exploration campaigns, and by learns for educational
purposes.

2.1. *Research status*

Rock minerals are mainly formed inorganically. Their daily use af-
fects all levels of society. They play a crucial role basic needs in sector
such as food, housing, clothing, energy and others. However, the fact
that they exist within rocks makes it difficult to unovering most of their
characteristics. Generally, they can be accessed by microscopy following
a resizing of the minerals. Microscopic observation of a thin section
shows that rocks are aggregates of one or more minerals in varying
quantities. According to Brown and Lowe (2003), when images of a thin
section cover its entire surface, they create a composition of images,
known as a ‘photomicrograph’ of a thin section. Optical mineralogy
highlights the interaction of minerals with light and their optical prop-
erties in thin sections (Hall, 1978). This operation gives each mineral its
own distinct optical properties, which enable it to be characterised. By
recognising the minerals in a rock, geologists, academics and geology
practitioners can understand the origin of the rock (Egger, 2005). Once
recognised, these researchers proceed to a classification. The first
methods focused on extracting the characteristics of a certain number of
parameters such as the geometric parameters of the grains, the colours,
the shapes, and so on (Ładniak and Młynarczuk, 2015; Budennyy et al.,
2017; Izadi et al., 2017). Baklanova and Baklanov in 2016 used cluster
algorithms and morphological analysis to determine the colours and
shapes in order to calculate the composition of the rocks. The method
uses micrographs without providing the number of mineral types or the
precision of the micrographs (Baklanova and Baklanov, 2016; Izadi et al.
(2017) proposed a classification approach with a two-level cascaded
neural network. The model first recognised minerals based on colour
characteristics, then identified rejected minerals from the first level
based on light texture attributes (flat and cross). Maitre et al. (2019)
based on the results of their work, stated: ‘This is the first time that
computer vision using based on machine learning algorithms has suc-
cessfully automated the recognition of mineral grains from digital im-
ages acquired with a simple optical microscope’. The model uses simple
iterative linear clustering to generate superpixels to isolate of the sand
grains a task not possible with traditional segmentation techniques. The
limitations of the model, as highlighted by the authors lie in the prov-
enance (regions) of the mineral grains.

Some authors have used thresholding to delimit distinct phases based
on colour contrast, intensity in the digital rock community (Prodanovic et al., 2015). This approach makes it difficult to automatically differentiate between phases with similar colours and intensities. The same reasons are often observed with segmentation algorithms, which sometimes benefit from manual control, even though they operate within an automatic framework. Purswani et al. (2020) state in their work that: 'Although Deep Learning methods can achieve promising segmentation results, their ability to learn features relies heavily on a large number of training images as well as high quality manual annotations' (Garcia-Garcia et al., 2017; Kendall et al., 2016; Nanfack et al., 2018). The work of (Baykan and Yılmaz, 2010) identified the same problems, suggesting the use of a large dataset of images to train the network for more reliable results. The limitations of the CNN-based segmentation method are linked to the small amount of data available for training, to avoid the phenomenon of over-learning. Segmentation also results in the elimination of certain backgrounds in the image. A method proposed by Li and Chen (2015) provides coherent pixels regions for calculating local features, thereby reducing the complexity of processing tasks (Li and Chen, 2015).

These initial approaches for characterising minerals work better but also raise a problem of dependency between the instances of minerals within a simple image. Previous several label prediction tasks have shown a high label dependency (Xue et al., 2011). For example, it is common to see a boat and water together, but seeing water and a bus together is quite rare. These methods focus on classifying labels of simple elements. Singh's team in 2022 used a CNN to categorise images of 'Biotite' and 'Quartz' (Singh et al., 2022). The authors created a high quality, noise-free dataset with a separate folder for each class. Notably, they report using photos obtained using the 'Bing imagedownloader' API, which was more time-consuming to process than ordinary 'jpeg' images. They suggested that the results could be more accurate if GPU clusters are used to stack the model as additional layers. In the case of Zou, Guo, Luo and Long, these authors worked on single-object classification tasks for photographs of mineral samples (Zhang et al., 2022). They assumed that only one mineral was present in each mineral-type photograph, with does not reflect the natural distribution patterns of minerals. Other strategies, such as graphical models to represent label dependency and co-occurrence dependencies, have not produced satisfactory results (Xue et al., 2011). The initial idea behind these approaches was to learn higher-order correlations between labels (Xue et al., 2011; Li et al., 2014). However, the lack of effective use of

previous knowledge regarding the probabilities of occurrence of associated minerals prevents existing models from accurately considering the relationships between various minerals in a given image. In fact, a single object label in an image is little relevance in many real-world application datasets. The number of object labels provided by an image varies from one image to another. As seen in the case of microscopic images of rock minerals (Fig. 1). Understanding a Multi-Label image therefore requires the formation of semantic information and its label dependencies. To resolve some of the challenges associated with this, we propose Multi-Label classification approach. Multi-label image recognition is a difficult but practical computer vision task. In recent years, Multi-Label categorization based on deep learning of image data has proven itself considerably in various image-related applications, such as semantic analysis, segmentation (Fu et al., 2019), object detection and recognition, and also activity detection (Zhang et al., 2019).

The search for a solution to the multi-label classification problem consists of efficiently learning the significant feature of each object in the input image. Thanks to the large capacity of CNN feature representations (Cao et al., 2019) and a availability of large labeled data such as ImageNet (Ouyang et al., 2017). Deep learning models have made significant progress in solving Multi-Label classification problems. In 2020, Contreras and his colleagues introduced a Multi-Label classification approach for mineral mapping in the analysis of hyperspectral core data (Contreras et al., 2020). The classification chain (CC) method was used with the Random Forest (RF) algorithm as classifier. WU and this team (2022) employed a hybrid model based on transformer decoder architecture and the Multi-Label classification method, identifying several minerals with 85.26 % accuracy (Wu et al., 2022). Their model was also able to approximate the location of identified minerals. The study tested and selected the most suitable network between MobilNet-V2 (Sandler et al., 2018), Gros transfert-m (Kolesnikov et al., 2020), ResNet-101 (He et al., 2016) and ViT-B/16 (Dosovitskiy et al., 2021) as feature extractors. In another study, Seo and Yun, (2023) used Multi-Label classification method to classify whole rocks (Granite, Diorite, Gabbro, Rhyolite, Andesite and Balsalt) on microscopic images of thin sections. Their method uses the ResNet-152 model and provides 90 % accuracy.

## 2.2. Evaluation metrics

In a multi-label classification task, Average Precision (AvP) (Pascanu

<figure><img src="image_3.png" /><figcaption>Fig. 1. Microscopic image of a thin section of basic tuff containing several minerals (Krah et al., 2024).</figcaption></figure>
et al., 2013; Pedregosa et al., 2011), evaluates the average fraction of labels classified above a particular label within y. It efficiently assesses the performance of different architectures while providing the prediction value for each label. The AvP calculation requires the average precision of each mineral category and is based on the 'Precision' and 'recall' variables. When AvP = 1, this means that the labels are perfectly sorted. As for the other metrics, they show a better quality of label classification for values lower than 1.

In artificial intelligence, 'positive' and 'negative' samples are defined according to the probability ranking of the classification result. As a result, positive samples that correspond to the truth in the field are called 'true positives (TP)' and those that do not are called 'false positives (FP)'. As for the negative samples, those that are identical to the truth are called 'true negatives (VN)' and are opposed to a final group called 'false negatives (FN)'. 'TP', 'FP', 'TN' and 'FN' together make up the total number of samples (TNS). Thus the metrics useful for evaluating our model are defined by the equations:

$$TNS = TP + FP + TN + FN \quad (1)$$

$$precision = \frac{TP}{TP + FP} \quad (2)$$

$$Recall = \frac{TP}{TP + FN} \quad (3)$$

$$Acuacy = \frac{TP + TN}{TNS} \quad (4)$$

$$F1score = 2 \times \frac{precision*Recall}{precision + Recall} \quad (5)$$

$$AvP = \sum_{n} (R_{n} - R_{n-1}) P_{n}, (n > 0) \quad (6)$$

Where, $P_n$ and $R_n$ are the precision and recall at the nth threshold.

## 2.3. ResNet and transfer learning

The central concept of Artificial Intelligence (AI) systems allows us to perceive our environment and take actions based on different perceptions. Computer vision focuses on this aspect of visual perception. It is the science of perceiving and understanding the world through images and/or videos. This is achieved by creating physical models of the world that enable intelligent systems to act appropriately. Training a neural network from scratch is a challenging task. It requires substantial expertise, along with access to large volumes of data, effective pre-processing, significant computational time, high costs, and the ability to extract new information. Under such circumstances, it is often advisable to use a pretrained network that has already been construted and trained, possibly on a different domain. In a short space of time, it is acquired and used as a starting point to create another network with a view to solving a target problem, even with a small amount of data. This approach is called transfer learning (Agarwal et al., 2021; Pan and Yang, 2009; Tan et al., 2018). This technique works by transferring the knowledge acquired from one problem to another through the use of these pre-trained models. Deep networks have delivered better performance, and their unique strength lies in their architecture design. The deep learning network's architecture generally consists of convolution, pooling and fully connected (FC) layers. It operates as follows.

* The convolution layers receive the input images and use their convolution filters to extract features, with the parameters updated through an error backpropagation process.

* Then, the pooling layers reduce the size of the input and output of the convolutional layers, creating feature maps. These maps then become inputs for the activation function ('softmax' or 'sigmoid') for the following layers.

* The fully connected layers contribute to the classification by flattening the feature maps for the final output.

As mentioned earlier, transfer learning shows convincing results on relatively small datasets. For slightly larger amounts of data, this same technique helps to optimize the pre-trained model.

# 3. Materials and methods

## 3.1. Acquisition of microscopic images and creation of the image database

Our database is based from natural images of thin sections of magmatic rock (Krah et al., 2024). A total of rock samples were collected and used to prepare 15 thin sections. The details of the various rock types are presented in Table 1. It is noteworthy that the majority of samples are granitic. This predominance reflects the geological context of Ivory Cost, which lies within the West African Craton and displays distinctive characteristics of the paleoproterozoic domain. In particular, the region is marked by an abudance of greenstone belts encircling granitoid bodies, derived from a substantial oceanic crust. The melting of this crust led to the generation of a large volume of granitic rocks (Doumbia et al., 1998). The area also comprises other geological formations, including migmatites, granulites, granulitic grey gneisses, and charnockites (Kouamelan et al., 1997), as well as volcanic rocks of tholeiitic and calc_alkaline affinities, volcano-sedimentary sequences, and sedimentary units (Plumb, 1991).

Each thin section was examined under homogeneous polarized light using a JEULIN microscope with a rotating stage and a monocular head. An MD500 camera (using Amscope 3.7 software) was mounted on top of the microscope to acquire the images. The images were acquired and stored on a computer (intel(R) Coe (TM) i5-8265U CPU @ 1.60 GHz 1.80 GHz, RAM size 16.0 GB (15.8 GB useable)). All images were captured in RGB format using an optical magnification of 4/0.10 and 160/-. Each raw image was saved as a "jpg" file with a resolution of 2592 x 1944 pixels. On Amscope 3.7, exposure and white balance settings were set to automatic mode, with an exposure time of 15.25 ms. Default values were used for color and image adjustments:"0 for hue, brightness, and gamma; "64" for saturation, and "32" for contrast. The black level correction remained fixed at a value of "5". As additional options, the sharpness was set to "3" and denoising to "0". Images were acquired sequentially under both polarized and non-polarized light to maximize the information obtained from each thin section or mineral. Given that minerals have different extinction properties, all images were captured at multiple rotation angles from 0° to 360°, at 45° interval. Ultimately, more than 3150 microscopic images captured under polarized light were acquired, constituting the core of database. Among these images, 2336 are predominantly multilabel. The data base was randomly divided into two groups. The first group, used to train the network for the model, consisting of 3086 images, with 80% allocated for training and 20% for validation. The second group (64 images) was

<table><caption>**Table 1**
Number of igneous rock samples by class and their crystalline granularity.</caption><thead><tr><td>Quantity</td><td>Sample description</td><td>crystalline granularity</td></tr></thead><tbody><tr><td>1</td><td>Biotite-amphibole granodiorite</td><td>Medium-grained</td></tr><tr><td>1</td><td>Biotite-alkali granite</td><td>Medium-grained</td></tr><tr><td>2</td><td>Biotite granite</td><td>Plagioclase phenocrysts</td></tr><tr><td>1</td><td>Dolerite</td><td>Ultrafine grained</td></tr><tr><td>1</td><td>Biotite-muscovite granite</td><td>Medium-grained</td></tr><tr><td>2</td><td>Biotite-amphibole granodiorite</td><td>Coarse grind</td></tr><tr><td>1</td><td>Biotite monzonitic granite</td><td>Coarse grind</td></tr><tr><td>1</td><td>Gabbro</td><td>Medium-grained</td></tr><tr><td>1</td><td>Basic tuff</td><td>—</td></tr><tr><td>4</td><td>Microgabbro</td><td>Fine ou micro-grained</td></tr></tbody></table>
used to test our model. All images were cropped to 224 × 224 pixels to simplify calculations. **Additionally, the training data was augmented by a rotation followed by reflection.** But initially, each image was labeled in order to define the minerals that constitute it by class. Seven (7) mineral classes were chosen to develop this study. Fig. 2 shows the histogram of labels per class.

## 3.2. Data preprocessing

The objective of this step is to obtain standardized image sizes suitable for convolutional neural network (224 × 224 pixels). To achieve this, a first customized function was developed to load images from a specified folder, on which resizing is to be applied. This marks the beginning of the preprocessing pipeline. This function includes a parameter that creates a new directory based on the parent folder, including subdirectories, filters, files by extension, and automatically assigns labels based on the parent directory of each image. This setup allows for the management of a large number of images. A second function checks the numbers of arguments defined by the user (in this case, a single input file), and initializes the corresponding parameters. Its main purpose is to resize the raw images. If no specific file is indicated, all files in the folder are considered. Moreover, this function extracts relevant information from each image, such as labels, full file paths, the complete list of classes, and class names in text format. It also displays a progress bar, verifies the dimensions of the resized images, and creates a destination folder organized into class specific subfolders. Class indices are determined, images are read, and finally resized to 224 × 224 pixels. This constitutes a simple resizing procedure. Authors such as Zawadzka and al (Zawadzka-Gosk et al., 2019), have conducted their work under similar preprocessing settings. In contrast, Su and al (Su et al., 2020), performed image cropping before resizing all images to the same target dimensions. For their research of early blight and late blight disease recognition on potato leaves based on CNN algorithm, the authors resized the tomato leaf images from 256 × 256 pixels to 128 × 128 pixels before converting them to a different scale.

## 3.3. Multi-label classification framework

In machine learning, Multi-Label (or multi-output) classification is similar to a classification problem. But with one difference one image can be associated with several labels simultaneously. For example, a microscopic image of a thin section might contain both minerals (such as quartz, plagioclase, biotite and muscovite). As a result, each instance is

associated with a set of labels and is qualified as relevant, hence the term Multi-Label. In this study, we propose to use the problem adaptation algorithm method based on the modification of the ResNet50 pre-trained network to perform our multi-label classification. Once the data is processed through the network, the labels for each pixel are chosen according to the frequency of existing minerals. This process involves mapping the inputs x into binary vectors y, where each label in y is assigned a value "0" or "1" for each label in y. Problem adaptation approaches directly modify the classification algorithms to account of the multi-Label nature of the data. With the ResNet network, modifying the output layers and the loss function has made it possible to manage several labels per image. These labels are derived from the segmentation results of the various images.

## 3.4. Segmentation

Segmentation is defined as the partitioning of a scene into various components. Its main objective is to identify groups of pixels that share common characteristics. Semantic segmentation is used when it is not possible to distinguish between different instances of the same object (Cordts et al., 2016). Segmentation leverages the extracted information to accurately identify a specific object or region of interest within an image (Shivhare and Gupta, 2015). During the extraction of meaningful information, recognition systems rely on feature vectors. These vectors enable shape recognition (Dupas, 2009), with each acting as a distinct shape signature. A major challenge at this stage lies in selecting the most appropriate features to optimize classification accuracy. In our approach, the segmentation process groups pixels based on predefined criteria (such as shape, texture, color, or pattern) to identify homogeneous regions and their boundaries within the image. In other words, segmentation simplifies the image representation into fundamental elements that are more relevant and easier to analyse. Its complexity and diversity justify the use of numerous techniques for its application (Lecoeur and Barillot, 2007).

# 4. Experimentation and analysis of results

## 4.1. Experimentation

In this study, we chose the ResNet50 network, a deep learning architecture. This choice was due to the fact that among current proposed networks, ResNet models (He et al., 2016) use their residual structure to improve network performance. This aspect allows for an increase in the depth of the layers while avoiding the disappearance of the gradient. ResNet models are among the most commonly used networks for feature extraction. With ResNet50, the input image was resized to 224 × 224 pixels, and then the initial convolution layers extracted low-level features from these input images. The residual blocks enable the efficient learning of deeper features. The fully connected layer, which transforms features into scores is replaced with a modified layer containing a number of neurons equal to the number of classes which is seven (7). Additionally, the softmax layer responsible for converting scores into probabilities for each class, it has been replaced by a 'sigmoid' layer to enable multi-label output, i.e. to provide independent probabilities for each class. Finally, the final classification layer, based on probabilities, is called the loss layer using binary entropy loss. Its role is to adapt the network to the multi-label task. The proposed model was implemented on the MatLab 2021b platform. It was trained for 10 epochs with a batch size of 32. The parameters were updated with the 'sgdm' optimizer using a learning step of $5 \times 10^{-4}$ (Qian, 1999). 'ImageAugmentation' with the operations "RandRotation" and "RandXReflection" (Cubuk et al., 2020) were used to improve the performance of our model on mineral images during training. Rotation is applied randomly around the z-axis, followed by reflection along the x-axis. This corresponds to rotation within the crystallographic (a,b) plane and subsequent inversion along the horizontal axis, equivalent to a crystallographic or optical inversion

<figure><img src="image_4.png" /><figcaption>Fig. 2. Number of labels per class.</figcaption></figure>
relative to the thin. The dataset was then split into training, validation, and test subsets. Out of a total of 3087 images, the distribution was as follows: 2469 images for training (80 %), 309 images for validation (10 %) and 309 images for testing (10 %). After on line data augmentation, the number of training images remained at 2469. To evaluate the performance of our model, a set of 50 new images was used for testing. Fig. 3 illustrates some training sequences and the architecture of Resnet50.

## 4.2. Experimental evaluation

In this section, we present the results of experiments with the datasets (training and validation) and of tests to evaluate the performance of the model generated for this task of multi-label classification of magmatic rock mineral images. Some representations will be used to illustrate the behaviour of our algorithm on training and validation datasets. This uses the problem adaptation method described earlier. The purpose of these experiments is to study the probability of the presence of minerals in multi-label learning algorithms.

### 4.2.1. Statistical analysis

Table 2 presents the probability score for the presence of minerals in each images following different tests of the recorded model in Fig. 4.

For each test conducted on an image A, B, C, D or E, the corresponding average values of the evaluation metrics are computed. Consistently exceeding 95 % across all metrics, these averages under-score the robustness and high predictive performance of the model. Therefore:

<table><caption>**Table 2**

Mineral probability scores for the test images from Fig. 4.</caption><thead><tr><th rowspan="2">Class</th><th colspan="5">Score</th></tr><tr><th>A</th><th>B</th><th>C</th><th>D</th><th>E</th></tr></thead><tbody><tr><td>Amphibole</td><td>6, 99.10<sup>-06</sup></td><td>3, 66.10<sup>-07</sup></td><td>1, 12.10<sup>-08</sup></td><td>0,99995</td><td>4, 38.10<sup>-05</sup></td></tr><tr><td>Biotite</td><td>0,2185</td><td>0,99195</td><td>0,99999</td><td>0,00033877</td><td>0,99985</td></tr><tr><td>Alkali feldspar</td><td>0,99995</td><td>0,0040888</td><td>0,9928</td><td>0,0023001</td><td>0,59763</td></tr><tr><td>Muscovite</td><td>0,081708</td><td>1</td><td>0,98847</td><td>1,25.10<sup>-07</sup></td><td>5, 83.10<sup>-07</sup></td></tr><tr><td>Plagioclase</td><td>0,99958</td><td>0,99995</td><td>1</td><td>9,69.10<sup>-05</sup></td><td>0,0036632</td></tr><tr><td>Pyroxene</td><td>1, 88.10<sup>-06</sup></td><td>2, 17.10<sup>-06</sup></td><td>9, 99.10<sup>-06</sup></td><td>0,99993</td><td>4, 62.10<sup>-05</sup></td></tr><tr><td>Quartz</td><td>0,99976</td><td>0,99999</td><td>1</td><td>6,83.10<sup>-05</sup></td><td>0,0012378</td></tr></tbody></table>

* The 'precision' shows the proportion of correct positive predictions. This means that when the model predicts that a mineral is present, it is correct about 97 % of the time on average.

* 'Recall' expresses the proportion of true positives (TP). In other words, it measures how often model accurately predicts the class. This means that, the model correctly identifies around 96 % of minerals on average.

* The 'F₁-score' reflects the robustness of the model by balancing precision and recall, with an average value of approximately 97 %.

Another metric, namely 'accuracy' denotes the proportion of total predictions that the model has correctly made. Our model demonstrates an average confidence level of 96 %. Overall, this means that, the model is very accurate.

In the same Table 2, the scores are analysed based on a confidence

<figure><img src="image_5.png" /><figcaption>**Fig. 3.** Sequences for image formation and architecture of the ResNet model in this study.</figcaption></figure>
<figure><img src="image_6.png" /><figcaption>Fig. 4. Test images: A-Alkali granite; B_Biotite-muscovite granite; C_Biotite-muscovite granite; D_Basic tuff; E_Biotite-alkali granite.</figcaption></figure>

threshold. A probability of presence threshold is generally set at 0.5 to give greater credibility to the model. However, when the prediction probability score values of the correlation labels are greater than 0.5, then the labels are relevant to the model. This means that the predicted labels are more consistent with the actual image scene. Below this threshold value, the labels are deemed ‘irrelevant’.

**Image A:** The model states with 99.95 % confidence that the image contains alkali feldspar, plagioclase and quartz. The other labels are not relevant to the model because the score values are less than 0.5. Amphibole and pyroxene are virtually non-existent in this image according to the model.

**Image B:** The model shows the presence of biotite, muscovite, plagioclase and quartz. However, the model has a confidence level of 100 % for muscovite.

**Image C:** The model states with 100 % confidence that the image contains plagioclase and quartz. This gives a score of ‘1’ for both minerals. Biotite, alkali feldspar and muscovite are predicted with over 98 % confidence. With respective score values of 1, 1259.10⁻⁰⁸ and 9, 9935.10⁻⁰⁶, the model signals with relevance the absence of amphibole and pyroxene in this given image.

**Image D:** The model detects the presence of amphibole and pyroxene with 99.99 % confidence. The other minerals have a pronounced

<figure><img src="image_7.png" /><figcaption>Fig. 5. Confusion matrices of predicted and true binary labels by class.</figcaption></figure>
irrelevance, reduced by a factor of $10^{-03}$.

**Image E:** The model states with over 99 % confidence for biotite. Alkali feldspar is the second mineral confirmed, but is score close to the threshold (59.76 %). The other minerals are less relevant to the model.

Fig. 5 shows a set of confusion matrices for the dataset validation set based on the proposed method. It provides overall performance as well as classification specific metrics, such as ‘Precision’, ‘Recall’ and ‘F_1-score’, which are capable of providing a precise assessment of a model’s performance. Apart from the amphibole and pyroxene classes, which show significantly low values for false negatives (FN (last row, first column) and false positives (FP (first row, second column), the others classes have high values starting at 25. The almost of hue (colour texture) between biotite and muscovite under polarised light shows the confusion between these classes, reflected in their FN and FP values. The same observation applies to quartz, plagioclase and alkaline feldspar, which share almost the same hues (colour texture) under polarised light. However, the model prediction linked to each matrix per class gives satisfactory results as a function of the number of labels per class (Fig. 2). For example, for a total of almost 680 labels for the amphibole class, 641 labels were correctly predicted by the model. For the plagioclase class, 1838 labels were well predicted out of almost 1900.

Each matrix follows the structural arrangement of the architecture, including the notations: “TP”, “FP”, “TN”, and “FN”, in the specified order. Using Table 3, let’s evaluate the model against the values provided by this series of confusion matrices. With a total of 3086 images, we have:

<figure><img src="image_8.png" /><figcaption>Fig. 6. Original image and the segmented constituent minerals.</figcaption></figure>

## 4.2.2. Visual analysis

### 4.2.2.1. Results of the segmentation.
Segmentation, a critical stage in image processing, delineates boundaries between distinct regions to isolate target objects. In the original microscopic image (Fig. 6), plagioclase, biotite, and quartz were identified, with segmentation yielding the following observations:

The approach produced approximative segmentation, likely due to limited image data (labels per class) or the decision to treat each mineral as a single entity.

### 4.2.2.2. Results obtained using the Grad-CAM algorithm.
When training a neural network (provided images and calculated output), it is expected that the value of the ‘Loss’ function will decrease during training and that, on the contrary, the ‘precision’, ‘recall’ and ‘accuracy’ functions will increase. However, these metrics do not provide any insight into how the network processes the image to produce the results. So, to better understand and characterise the potential of our proposed approach, we use a third evaluation method, which is the cartographic method. This method is based on the Grad-CAM algorithm known as gradient-weighted class activation mapping (Selvaraju et al., 2017). It is part of the model evaluation methods and is a visualisation technique for understanding how a model was led to decide. It displays an overview of what is happening in the neural network.

Fig. 7 illustrates the area (s) of the image responsible for predicting a

<figure><img src="image_9.png" /><figcaption>Fig. 7. Class activation maps of associated minerals based on Grad-CAM.</figcaption></figure>

<table><caption>**Table 3**
Model performance evaluation metrics.</caption><thead><tr><th></th><th>Amphibole</th><th>Biotite</th><th>Feldspath</th><th>Muscovite</th><th>Plagioclase</th><th>Pyroxène</th><th>Quartz</th></tr></thead><tbody><tr><td>TP</td><td>641</td><td>1257</td><td>616</td><td>449</td><td>1838</td><td>727</td><td>1184</td></tr><tr><td>FP</td><td>06</td><td>53</td><td>25</td><td>34</td><td>30</td><td>03</td><td>35</td></tr><tr><td>FN</td><td>13</td><td>33</td><td>60</td><td>41</td><td>29</td><td>06</td><td>43</td></tr><tr><td>TN</td><td>2426</td><td>1743</td><td>2385</td><td>2562</td><td>1189</td><td>2350</td><td>1824</td></tr><tr><td>«Precision»</td><td>99,07</td><td>95,95</td><td>96,09</td><td>92,96</td><td>98,39</td><td>99,58</td><td>97,12</td></tr><tr><td>«Recall»</td><td>98,01</td><td>97,44</td><td>91,12</td><td>91,63</td><td>98,44</td><td>99,18</td><td>96,49</td></tr><tr><td>«Accuracy»</td><td>97,47</td><td>97,21</td><td>97,24</td><td>97,56</td><td>98,08</td><td>99,70</td><td>97,47</td></tr><tr><td>F<sub>1</sub>-score</td><td>98,53</td><td>96,68</td><td>93,53</td><td>92,29</td><td>98,41</td><td>99,37</td><td>96,80</td></tr></tbody></table>

The evaluation metrics as whole show values of over 90 %. These values show that our method chosen for this multi-label classification task is robust in predicting minerals.
particular category. These areas of interest are known as probability *heatmaps*. However, the heat maps show the importance of each pixel with the class of interest. Therefore, if the object corresponding to the label exists in the image, the corresponding relevant feature region is clearly highlighted (multicoloured spots).

## 5. Discussion

Following various analyses linked to the experiments carried out, we were able to highlight the performance of the proposed method. Based on the results obtained for this Multi-Label classification task model, it is clear that the application of convolutional neural networks represents a significant advance in the field of rock mineral identification. With an average precision of 97.15 %, an average recall of 96.25 % and an F1_score of 96.69 %, our model stands out from other Multi-Label classification methods. The scores recorded provide a distribution that respects the logic of differentiation between mineral phases in nature. The accuracy result is clearly superior to that of the method proposed by James and his peers (Lakshmi, 2022). The authors used a CNN architecture coupled to a long-term memory (LSTM) to solve a label dependency problem. However their model produced fewer results than the ResNet101 structure combined with a binary relevance cost function (Ouyang et al., 2017) on the NUS-WIDE dataset. NUS-WIDE is a multi-object web image database (Russakovsky et al., 2015). This advancement in our model is based on the residual structure of the ResNet (He et al., 2016). This structure improves network performance by increasing the depth of the layers to prevent the gradient from exploding (or disappearing). ResNet50 is among the most widely used convolutional neural networks for feature extraction. Seo and Yun, (2023) proposed a model based on the ResNet152 architecture that efficiently identifies features in microscopic rock images, leading to reliable rock classification.

Another strength of our model lies in the modification of the output layer. The softmax function switches to the sigmoid function to identify several minerals in the input image.

Moreover, the confusion matrix represents a practical case of model evaluation. Thus, the few values considered high (60 for 'FN' in favour of alkali feldspar and 53 for 'FP' in favour of biotite) are respectively due to the confusion of hues (texture and colour texture) between biotite and muscovite on one hand, and similarity between quartz, plagioclase and alkali feldspath. Alkali feldspath is sometimes confused with quartz/ orthoclase under polarised light. Additionally, since the images originate the same source (magmatic rocks), they share similarities in terms of textures and hues. We suggest that these high predictions indicate that the model classified the labels as belonging to the same origin. However, the overall results show that the CNN model can adequately identify the labels present in microscopic rock images. Confusion matrices appear to be an indicator for reducing the error rate in assessment, thereby improving the classification performance of a CNN model. Liu et al. (2021), have shown in their study that by integrating attention blocks (SE block, CA block, SA block, MA block) into the ResNet101 model, the model error rate could be effectively be reduced thanks to the results provided by the confusion matrices.

The GradCAM method supports our prediction results by visually establishing the correlation between important regions of the image and their labels (Selvaraju et al., 2017). GradCAM provides a practical illustration of a visual interpretation of the areas that CNNs focus on during predictions (Selvaraju et al., 2020). Specifically, the algorithm visualises the learned characteristic map. This means that if the object corresponding to the label exists in the image, GradCAM clearly highlights the relevant region. In their work, Zhao et al. have introduced a module called SAM which efficiently learns the salient features of the image and accurately locates the position of objects (Zhao et al., 2020). The same algorithm was used to identify quartz, hematite and chalcopyrite in a photographic image of multi-mineral rocks (Wu et al., 2022). The authors used a model that first extracts the mineral characteristics

from the images, then correlates these characteristics with known minerals using a transformer decoder. The multi-label image classification model achieved an average accuracy of 85.26 % and was able approximately locate the minerals in question.

From this discussion, it appears that a multi-label classification approach provides more descriptive and meaningful results than a single-label classification where the mapping is less detailed. This work present a promising opportunity for applications in rock mineral identification offering a tool that could extend from managing geological analyses right through to the academic world, providing more accurate and relevant rock mapping probabilities.

## 6. Conclusion

This work proposes a method for recognising minerals using microscopic images of rock thin sections. It uses the multi-label classification technique. The ResNet50 architecture was used to extract mineral feature based on the hues and colour texture resulting from the phenomenon of birefringence. It is reflected in the performance achieved with convolutional neural network (CNN). Three static evaluation metrics, precision, recall, and F1_score yielded average scores exceeding 96 %. This indicates that the proposed approach effectively highlights both the detection of regions of interest and the robustness of CNNs. The Grad-CAM algorithm, through various visualizations, provided deeper insights into the feature extraction capabilities of CNN models. It thus demonstrates the ability of convolutional neural network to capture distinct and relevant visual attributes within mineral rock images. However, the lack of dataset diversity beyond mineral images often hinders precision. For instance, the model occasionally misclassifies an image of a flower (green in color) as biotite or amphibole (green hornblende), among others. We will also test our application on the same minerals in sedimentary and/or metamorphic rocks. Adopting a serial mineral based approach may enhance model performance and improve segmentation accuracy.

### Data availability statement

The corresponding author will provide datasets created and/or analysed during this work upon reasonable request.

### CRediT authorship contribution statement

**Kouadio Krah:** Writing – review & editing, Writing – original draft, Visualization, Validation, Supervision, Software, Resources, Methodology, Investigation, Funding acquisition, Formal analysis, Data curation, Conceptualization. **Sié Ouattara:** Visualization, Validation, Supervision, Software, Methodology, Formal analysis. **Gbele Ouattara:** Visualization, Validation, Supervision, Methodology, Formal analysis. **Marc Euphrem Allialy:** Visualization, Validation, Supervision, Formal analysis. **Alain Clement:** Visualization, Validation, Supervision, Software, Formal analysis.

### Declaration of competing interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

### Acknowledgements

The authors thank the University of Man, Côte d'Ivoire, for providing microscopes for the microscopic imaging of the rocks. Many thanks to the EDP of Yamoussoukro (Côte d'Ivoire) for its logistical and human support in carrying out this work.
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