Query: MIAS classification

title: Classification of Mammogram Images Using Multiscale all Convolutional Neural Network (MA CNN)
abstract: Breast cancer is one of the leading causes of cancer death among women in worldwide. Early diagnosis of breast cancer improves the chance of survival by aiding proper clinical treatments. The digital mammography examination helps in diagnosing the breast cancer at its earlier stage. In this paper, Multiscale All Convolutional Neural Network (MA CNN) is developed to assist the radiologist in diagnosing the breast cancer effectively. MA CNN is a convolutional neural network based approach that classifies mammogram images accurately. Convolutional neural networks are excellent in extracting the task specific features, since the feature learning is associated with classification task in order to attain the improved performance. The proposed approach automatically categorizes the mammographic images on mini MIAS dataset into normal, malignant and benign classes. This model improves the accuracy of the classification system by fusing the wider context of information using multiscale filters without negotiating the computation speed. Experimental results show that MA CNN is a powerful tool for diagnosing breast cancer by means of classifying the mammogram images with overall sensitivity of 96% and 0.99 AUC.

title: A novel classification scheme to decline the mortality rate among women due to breast tumor
abstract: Early screening of skeptical masses or breast carcinomas in mammograms is supposed to decline the mortality rate among women. This amount can be decreased more on development of the computer aided diagnosis with reduction of false suppositions in medical informatics. Our aim is to provide a robust tumor detection system for accurate classification of breast masses using normal, abnormal, benign, or malignant classes. The breast carcinomas are classified on the basis of observed classes. This is highly dependent on feature extraction process. In propose work, a novel algorithm for classification based on the combination of top Hat transformation and gray level co occurrence matrix with back propagation neural network. The aim of this study is to present a robust classification model for automated diagnosis of the breast tumor with reduction of false assumptions in medical informatics. The proposed method is verified on two datasets MIAS and DDSM. It is observed that rate of false positives decreased by the proposed method to improve the performance of classification, efficiently.

title: Intellectual detection and validation of automated mammogram breast cancer images by multi class SVM using deep learning classification
abstract: Breast cancer is an important cause of death in females. Early recognition of this disease with the assistance of mammography reduces the death rate. Deep learning (DL) is an approach being utilized and requested by radiologists to assist in making an accurate diagnosis, and it can help to improve outcome predictions. This paper includes a new approach, applied on the Mini MIAS dataset of 322 images, involving a pre processing method and inbuilt feature extraction using K means clustering for Speed Up Robust Features (SURF) selection. A new layer is added at the classification level, which carries out a ratio of 70% training to 30% testing of the deep neural network and Multiclass Support Vector Machine (MSVM) The outcome described herein demonstrates that the accuracy rate of the proposed automated DL method using K means clustering with MSVM is improved as compared with a decision tree model. Experimental results show that the average accuracy (ACC) rates of the three classes, i.e. normal, benign and malignant cancer, using the proposed method, are 95% 94% and 98% respectively. The increased sensitivity rate is 3% specificity is 2% and Receiver Operating Characteristics (ROC) area is 0.99 using SVM compared to the Multi Layer Perceptron (MLP) and J48+K mean clustering WEKA manual approach. A 10 fold cross validation was used, and the obtained results for the Support Vector Machine (SVM) K nearest neighbour (KNN) linear discriminant analysis (LDA) and Decision Tree were 96.9% 93.8% 89.7% and 88.7% respectively.

title: Intellectual Detection and Validation of Automated Mammogram Breast Cancer Images by Multi Class SVM using Deep Learning Classification
abstract: Breast cancer is an important cause of death in females. Early recognition of this disease with the assistance of mammography reduces the death rate. Deep learning (DL) is an approach being utilized and requested by radiologists to assist in making an accurate diagnosis, and it can help to improve outcome predictions. This paper includes a new approach, applied on the Mini MIAS dataset of 322 images, involving a pre processing method and inbuilt feature extraction using K means clustering for Speed Up Robust Features (SURF) selection. A new layer is added at the classification level, which carries out a ratio of 70% training to 30% testing of the deep neural network and Multiclass Support Vector Machine (MSVM) The outcome described herein demonstrates that the accuracy rate of the proposed automated DL method using K means clustering with MSVM is improved as compared with a decision tree model. Experimental results show that the average accuracy (ACC) rates of the three classes, i.e. normal, benign and malignant cancer, using the proposed method, are 95% 94% and 98% respectively. The increased sensitivity rate is 3% specificity is 2% and Receiver Operating Characteristics (ROC) area is 0.99 using SVM compared to the Multi Layer Perceptron (MLP) and J48+K mean clustering WEKA manual approach. A 10 fold cross validation was used, and the obtained results for the Support Vector Machine (SVM) K nearest neighbour (KNN) linear discriminant analysis (LDA) and Decision Tree were 96.9% 93.8% 89.7% and 88.7% respectively.

title: Breast Density Classification Using Local Quinary Patterns with Various Neighbourhood Topologies
abstract: This paper presents an extension of work from our previous study by investigating the use of Local Quinary Patterns (LQP) for breast density classification in mammograms on various neighbourhood topologies. The LQP operators are used to capture the texture characteristics of the fibro glandular disk region F G D r o i instead of the whole breast area as the majority of current studies have done. We take a multiresolution and multi orientation approach, investigate the effects of various neighbourhood topologies and select dominant patterns to maximise texture information. Subsequently, the Support Vector Machine classifier is used to perform the classification, and a stratified ten fold cross validation scheme is employed to evaluate the performance of the method. The proposed method produced competitive results up to 86.13 and 82.02 accuracy based on 322 and 206 mammograms taken from the Mammographic Image Analysis Society (MIAS) and InBreast datasets, which is comparable with the state of the art in the literature.

title: Multiscale connected chain topological modelling for microcalcification classification
abstract: Computer aided diagnosis (CAD) systems can be employed to help classify mammographic microcalcification clusters. In this paper, a novel method for the classification of the microcalcification clusters based on topology/connectivity has been introduced. The proposed method is distinct from existing techniques which concentrate on morphology and texture of microcalcifications and surrounding tissue. The proposed approach used multiscale morphological relationship of connectivity between microcalcifications where connected chains between nearest microcalcifications were generated at each scale. Subsequently, graph connectivity features at each scale were extracted to estimate the topological connectivity structure of microcalcification clusters for benign versus malignant classification. The proposed approach was evaluated using publicly available digitized datasets: MIAS and DDSM, in addition to the digital OPTIMAM dataset. The classification of features using KNN obtained a classification accuracy of 86.47+ 1.30% 90.0+ 0.00% 82.5+ 2.63% 76.75+ 0.66% for the DDSM, MIAS manual, MIAS auto and OPTIMAM datasets respectively. The study showed that topological/connectivity modelling using a multiscale approach was appropriate for microcalcification cluster analysis and classification; topological connectivity and distribution can be linked to clinical understanding of microcalcification spatial distribution.

title: Extraction of spiculated parts of mammogram tumors to improve accuracy of classification
abstract: Spiculated parts of masses are significant features to classify tumors in digital mammography; however, segmentation, which is used to extract the shape and contour of a tumor, eliminates them. To address this problem, the current study proposes a novel algorithm for extraction of the spiculated pixels of a tumor that are of similar intensity along a line. It first applies the sums of the differences between the central pixel and neighboring pixels in different symmetric orthogonal directions. The minimum difference between two symmetric orthogonal directions specifies the similarity of pixels in one direction as denoting a spiculated part of the mass. These parts then are added to the segmented image to enhance the shape of tumor. The features of the tumor are extracted from the final segmented image to allow its classification as benign or malignant. Simulation results showed that the accuracy and the area under the ROC curve of the proposed method for mini MIAS and DDSM databases were 91.37% and 93.22% and 0.9776 and 0.9752, respectively. This confirms the effectiveness of the proposed algorithm for extraction of the spiculated parts of a malignant tumor with the aim of increasing the classification accuracy.

title: Comparison Analysis of Linear Discriminant Analysis and Cuckoo Search Algorithm in the Classification of Breast Cancer from Digital Mammograms
abstract: Breast cancer is the most common invasive severity which leads to the second primary cause of death among women. The objective of this paper is to propose a computer aided approach for the breast cancer classification from the digital mammograms.  Designing an effective classification approach will assist in resolving the difficulties in analyzing digital mammograms. The proposed work utilized the Mammogram Image Analysis Society (MIAS) database for the analysis of breast cancer. Five distinct wavelet families are used for extraction of features from the mammograms of MIAS database. These extracted features are statistical in nature and served as input to the Linear Discriminant Analysis (LDA) and Cuckoo Search Algorithm (CSA) classifiers.  Error rate, Sensitivity, Specificity and Accuracy are the performance measures used and the obtained results clearly state that the CSA used as a classifier affords an accuracy of 97.5% while compared with the LDA classifier.  The results of comparative performance analysis show that the CSA classifier outperforms the performance of LDA in terms of breast cancer classification.

title: Breast Density Classification using Local Septenary Patterns: A Multi resolution and Multi topology Approach
abstract: We present an extension of our previous work in  by investigating the use of Local Septenary Patterns (LSP) for breast density classification in mammograms. The LSP operator is a variant of Local Binary Patterns (LBP) inspired by Local Ternary Patterns (LTP) and Local Quinary patterns (LQP) The main extensions in our work are i) we investigate the use of a multi resolution technique when extracting micro texture information, ii) we investigate different neighbourhood topologies as different ways of extracting texture features, and iii) we use an additional dataset called InBreast as well as the most popular dataset in the literature, which is the Mammographic Image Analysis Society (MIAS) to further evaluate the performance of the LSP operator.

title: Fuzzy C means and region growing based classification of tumor from mammograms using hybrid texture feature
abstract: Identifying abnormality using breast mammography is a challenging task for radiologists due to its nature. A more consistent and precise imaging based CAD system plays a vital role in the classification of doubtful breast masses. In the proposed CAD system, pre processing is performed to suppress the noise in the mammographic image. Then segmentation locates the tumor in mammograms using the cascading of Fuzzy C Means (FCM) and region growing (RG) algorithm called FCMRG. Features extraction step involves identification of important and distinct elements using Local Binary Pattern Gray Level Co occurrence Matrix (LBP GLCM) and Local Phase Quantization (LPQ) The hybrid features are obtained from these techniques. The mRMR algorithm is employed to choose suitable features from individual and hybrid feature sets. The nominated feature sets are analysed through various machine learning procedures to isolate the malignant tumors from the benign ones. The classifiers are probed on 109 and 72 images of MIAS and DDSM databases respectively using k fold (10 fold) cross validation method. The enhanced classification accuracy of 98.2% is achieved for MIAS dataset using hybrid features classified by Decision Tree. Whereas 95.8% accuracy is obtained for DDSM dataset using KNN classifier applied on LPQ features.