# Hybrid Optimization and Explainable Deep Learning for Breast Cancer Detection
## 1 Introduction  
Breast cancer (BC) is the interruption of the normal growth of cells and the development of abnormal and uncontrolled cellular growth in breast tissue, which causes a tumor [1]. Despite this, it still accounts for the main cause of cancer-related deaths in women in 9 out of 10 countries, and there is an occurrence of more than 2.3 million new cases every year [2]; about 0.4 million female deaths every year. In particular, clinical outcomes are increasingly influenced by systemic immune-inflammation indices, especially in HER2-positive metastatic cases, where early stratification may guide treatment selection [3]. The disease arises from abnormal and uncontrolled growth of breast tissue cells, most commonly manifesting as ductal carcinoma among over 20 histological subtypes [4]. Key diagnostic features include architectural distortion, microcalcifications, and mass formation [5]. Early diagnosis significantly reduces mortality [6], with treatment options spanning surgery, radiation, and chemotherapy [7]. Despite advances in screening technologies [8], including spectroscopy-based methods such as surface-enhanced Raman spectroscopy using porous silicon Bragg reflectors for enhanced signal sensitivity in breastcancer detection [9], challenges persist due to diagnostic delays, noise in medical images, and interpretability limitations in AI-based systems [10; 11; 12]. Traditional image processing techniques, such as shape features, local binary patterns, and gray-level co-occurrence matrices, have improved discrimination in mammography [13]. However, deep learning (DL), particularly convolutional neural networks (CNNs), has demonstrated superior performance in medical image analysis with a 6% performance gain over classical methods [14; 15; 16; 17]. DL models demand extensive annotated datasets, computational resources, and expert knowledge constraints often unmet in clinical settings [18]. Transfer learning (TL), which repurposes pretrained CNNs for medical imaging, has thus emerged as a robust alternative [19; 20]. As imaging data grow in complexity, dimensionality reduction and feature selection (FS) techniques have become critical for improving model generalization and reducing noise [21; 22; 23; 24; 25]. Such biological and molecular complexity, including stress-induced molecular mechanisms affecting cellular systems, has implications for the interpretability of cancer models [26]. Metaheuristic FS algorithms such as FLA and DOA have shown effectiveness in boosting classification by selecting high-quality features [27; 28]. However, DL models often behave as black boxes, raising concerns regarding clinical trust and decision transparency [29; 30; 31; 32]. To address this, explainable AI (XAI) has introduced interpretability tools like Grad-CAM, which visually highlights image regions influencing model predictions [33; 34]. This is particularly valuable in healthcare, where understanding decision boundaries is crucial for clinical acceptance. Numerous studies have explored similar directions. * Development of a hybrid diagnostic framework using mammography and ultrasound images, constructing a diverse fusion dataset for binary breast cancer classification;
* Fine-tuning MobileNet using the Firefly Algorithm (FLA) and Dingo Optimization Algorithm (DOA) to optimize feature selection and improve classification performance;
* Comparative evaluation of multiple transfer learning models (CNN, MobileNet, Xception, DenseNet121, EfficientNet, ResNet50, and VGG16) to identify the most effective architecture for breast cancer diagnosis;
* Performance validation on three datasets: MIAS, Breast Ultrasound, and their fusion;
* Integration of Grad-CAM to generate interpretable visual explanations for model predictions;
* Demonstration of the framework's superiority over existing methods in terms of accuracy, precision, recall, and F1-score. For example, ensemble CNNs combining AlexNet, GoogLeNet, VGG16, and ResNet-50 enhanced microcalcification detection in mammograms [35].