An optimized model based on adaptive convolutional neural network and grey wolf algorithm for breast cancer diagnosis.

Breast cancer is one of the leading life killing cancers in women patients around the world. Digital mammogram is used to detect and segment the abnormal mass portions in breast region. In this article, the breast cancer regions are detected and segmented using the Gabor transform based Convolutional Neural Networks (CNN) classification approach. This proposed breast cancer detection method consist of the following modules Kirsch's edge detector, Gabor transform, CNN classification and Segmentation. The cancer pixels and healthy pixels are differentiated in edge pixels. Hence, the Kirsch's edge detector is used to detect the fine edge boundary pixelsthe in source mammogram image. Then, the spatial edge detected mammogram image is converted into multi resolution mammogram image using Gabor transform. This transformed multi resolution mammogram image is classified into Normal, Benign or Malignant in this article using the proposed CNN architecture. The developed CNN structure extracts more complex feature maps from their internal layers to improve the classification rate. The conventional and proposed CNN structure is differed with respect to the internal layers. The proposed CNN structure is optimized by reducing the usage of the internals and increases Convolutional Filters (CF) in each Convolutional Layer during utilization design process. After classification process over, the cancer regions in the abnormal mammogram images are segmented using morphological functions. The proposed breast cancer detection is evaluated on Mammographic Image Analysis Society (MIAS) dataset and their cancer region segmentation results are 98.4% of sensitivity, 98.9% of specificity and 99.2% of cancer region segmentation accuracy. The average Detection Rate (DR) of the proposed breast cancer detection method is about 98.3% on the set of mammogramthe images from MIAS dataset. These simulation results are compared with other state‐of‐the art methods and cross verified by the k‐fold verification algorithm.

Breast cancer is a global health issue that necessitates precise classification for early detection and effective treatment. In recent years, pre-trained models have shown great potential in the field of medical image classification, including breast cancer classification. These models have been trained on extensive datasets, and they possess the ability to capture intricate features and patterns within medical images, facilitating accurate classification. However, some of the models are non-generic. They can be sensitive to dataset biases, leading to over fitting on specific patterns present in the training data, and they equally struggle to handle data from different distributions. In this work, we proposed a generic hybrid model for image classification. The features were extracted from two datasets: the mammographic image analysis society (MIAS) and the INbreast dataset, respectively, through the pre trained EfficientNetB2 architecture. However, three classifiers were used in the image classification of the extracted features: MGSVM, CUBIC SVM, and XGBOOST. Eight evaluation metrics were selected to assess the performance of the proposed models. These metrics include accuracy, precision, F1-score, AUC, sensitivity, false negative rate (FNR), Kappa score, and time complexity. Experimental results show that the hybrid of EfficientNetB2 and the MGSVM classifier is more generic and efficient for breast cancer diagnosis and classification. It exhibits a strong performance when classifying mammography breast images from both datasets, achieving impressive metrics such as an overall accuracy of 99.47%, a sensitivity rate of 99.31%, precision of 99.44%, F1-score of 99.44%, AUC of 99.44%, a low FNR (False Negative Rate) of 0.007, a kappa score of 0.98, and a manageable time complexity of 231.44 seconds on the MIAS Dataset.

Breast cancer is the most frequently diagnosed and leading cause of cancer-related mortality among women worldwide. The danger of this disease is due to its asymptomatic nature in the early stages, thereby underscoring the importance of early detection. Mammography, a specialized X-ray imaging technique for breast examination, has been pivotal in facilitating early detection and reducing mortality rates. In recent years, artificial intelligence (AI) has gained substantial popularity across various fields, including medicine. Numerous studies have leveraged AI techniques, particularly convolutional neural networks (CNNs) and You Only Look Once (YOLO)-based models, for medical image detection and classification. However, the predictions of such AI models often lack transparency and explainability, resulting in low trustworthiness. This study aims to address this gap by investigating three state-of-the-art versions of the YOLO algorithm-YOLO version 9 (YOLOv9), YOLO version 10 (YOLOv10), and YOLO version 11 (YOLO11)-trained on breast cancer imaging datasets, specifically the INbreast and Mammographic Image Analysis Society (MIAS) databases. Additionally, to address the challenges posed by the lack of explainability and transparency, we integrate seven explainable artificial intelligence (XAI) methods: Grad-CAM, Grad-CAM++, Eigen-CAM, EigenGrad-CAM, XGrad-CAM, LayerCAM, and HiResCAM. This study utilized two publicly available breast cancer image databases: INbreast: toward a Full-field Digital Mammographic Database and the MIAS dataset. Preprocessing steps were applied to standardize all images in accordance with the input requirements of the YOLO architecture, as these datasets were used to train the three most recent versions of YOLO. The YOLO model demonstrating the highest performance-measured by mean average precision (mAP), precision, and recall-was selected for integration with seven different XAI methods. The performance of each XAI technique was evaluated both qualitatively through visual inspection and quantitatively using several metrics, including matching ground truth (mGT), Pearson correlation coefficient (PCC), precision, recall, and root mean square error (RMSE). These methodologies were employed to interpret and visualize the "black box" decision-making processes of the top-performing YOLO model. Based on our experimental findings, YOLO11 outperformed YOLOv9 (mAP 0.868) and YOLOv10 (mAP 0.926), achieving the highest mAP of 0.935, with classification accuracies of 95% for benign and 80% for malignant cases. Among the evaluated XAI techniques, HiResCAM provided the most effective visual explanations, attaining the highest mGT score of 0.49, surpassing EigenGrad-CAM (0.45) and LayerCAM (0.42) in both visual and quantitative evaluations. The integration of YOLO11 with HiResCAM offers a robust solution that combines high detection accuracy with improved model interpretability. This approach not only enhances user trustworthiness by revealing decision-making patterns and limitations but also provide insights into the weaknesses of the model, enabling developers to refine and improve AI performance further.

Breast cancer is among the top causes of fatalities related to cancer in females. Radiologists commonly use mammogram images to detect breast tumors in their early stages. However, mammography can produce low-contrast images, making it difficult and time-consuming to segment abnormal regions. Deep convolutional neural networks (CNNs) are commonly used for image evaluations. This study used deep CNN models to develop a computer-aided diagnostic (CAD) system for feature extraction and classification. The proposed approach consists of three phases. In the first phase, a shallow, deep CNN model comprising five convolutional layers, five max-pooling layers, one batch normalization layer, and one dropout layer was developed and used to extract recombined images and novel features. In the second phase, the Inception-v3 model was used for label smoothing and classification due to its multiple filters with different sizes. In the third phase, features were extracted using shallow, deep CNN and Inception-v3 models. The Infallible Euclidean distance-based nonlinear dimensionality reduction approach was used to minimize dimensionality. Finally, the Gini-index-based C4.5 decision tree was used for the binary classification of mammogram images from the Digital Database for Screening Mammography (DDSM) + Curated Breast Imaging Subset of DDSM (CBIS-DDSM) and Mammographic Image Analysis Society (MIAS) datasets. The proposed hybrid shallow, deep CNN and Inception-v3 model achieved 99.52% accuracy, a 96% AUC on the DDSM + CBISDDSM dataset, and an accuracy of 97.53% and an AUC value of 97% on the MIAS dataset. Compared with other cutting-edge CAD systems, the proposed hybrid approach achieved higher accuracy by combining in-depth features across both datasets.

Breast cancer is the second leading cause of death in women; therefore, effective early detection of this cancer can reduce its mortality rate. Breast cancer detection and classification in the early phases of development may allow for optimal therapy. Convolutional neural networks (CNNs) have enhanced tumor detection and classification efficiency in medical imaging compared to traditional approaches. This paper proposes a novel classification model for breast cancer diagnosis based on a hybridized CNN and an improved optimization algorithm, along with transfer learning, to help radiologists detect abnormalities efficiently. The marine predators algorithm (MPA) is the optimization algorithm we used, and we improve it using the opposition-based learning strategy to cope with the implied weaknesses of the original MPA. The improved marine predators algorithm (IMPA) is used to find the best values for the hyperparameters of the CNN architecture. The proposed method uses a pretrained CNN model called ResNet50 (residual network). This model is hybridized with the IMPA algorithm, resulting in an architecture called IMPA-ResNet50. Our evaluation is performed on two mammographic datasets, the mammographic image analysis society (MIAS) and curated breast imaging subset of DDSM (CBIS-DDSM) datasets. The proposed model was compared with other state-of-the-art approaches. The obtained results showed that the proposed model outperforms the compared state-of-the-art approaches, which are beneficial to classification performance, achieving 98.32% accuracy, 98.56% sensitivity, and 98.68% specificity on the CBIS-DDSM dataset and 98.88% accuracy, 97.61% sensitivity, and 98.40% specificity on the MIAS dataset. To evaluate the performance of IMPA in finding the optimal values for the hyperparameters of ResNet50 architecture, it compared to four other optimization algorithms including gravitational search algorithm (GSA), Harris hawks optimization (HHO), whale optimization algorithm (WOA), and the original MPA algorithm. The counterparts algorithms are also hybrid with the ResNet50 architecture produce models named GSA-ResNet50, HHO-ResNet50, WOA-ResNet50, and MPA-ResNet50, respectively. The results indicated that the proposed IMPA-ResNet50 is achieved a better performance than other counterparts.

The classification of medical images is significant among researchers and physicians for the early identification and clinical treatment of many disorders. Though, traditional classifiers require more time and effort for feature extraction and reduction from images. To overcome this problem, there is a need for a new deep learning method known as Convolution Neural Network (CNN), which shows the high performance and self-learning capabilities. In this paper,to classify whether a chest X-ray (CXR) image shows pneumonia (Normal) or COVID-19 illness, a test-bed analysis has been carried out between pre-trained CNN models like Visual Geometry Group (VGG-16), VGG-19, Inception version 3 (INV3), Caps Net, DenseNet121, Residual Neural Network with 50 deep layers (ResNet50), Mobile-Net and proposed CNN classifier. It has been observed that, in terms of accuracy, the proposed CNN model appears to be potentially superior to others. Additionally, in order to increase the performance of the CNN classifier, a nature-inspired optimization method known as Hill-Climbing Algorithm based CNN (CNN-HCA) model has been proposed to enhance the CNN model’s parameters. The proposed CNN-HCA model performance is tested using a simulation study and contrasted to existing hybridized classifiers like as Particle Swarm Optimization (CNN-PSO) and CNN-Jaya. The proposed CNN-HCA model is compared with peer reviewed works in the same domain. The CXR dataset, which is freely available on the Kaggle repository, was used for all experimental validations. In terms of Receiver Operating Characteristic Curve (ROC), Area Under the ROC Curve (AUC), sensitivity, specificity, F-score, and accuracy, the simulation findings show that the CNN-HCA is possibly superior than existing hybrid approaches. Each method employs a k-fold stratified cross-validation strategy to reduce over-fitting.

Breast cancer detection and diagnosis using hybrid deep learning architecture

The architectural distorted regions in mammogram images are detected and segmented using computer aided hybrid classification approach in this paper. The main importance of this research work is to provide a computer aided methodology for screening the distorted regions in mammogram images. In present approach, the classification accuracy of the conventional methods is not suitable for further diagnosis process such as malignant and benign. Hence, the main objective of this paper is to develop an efficient architectural region detection method using soft computing method with high classification accuracy for further diagnosis purpose. This proposed method has two stages of the proposed flow as architectural distorted detected mammogram image and segmentation of architectural distorted regions in mammogram images. The first stage of this proposed method uses Random Forest (RF) classification method which classifies the source mammogram image into either normal or abnormal. In second stage of the proposed method, the abnormal image is further classified into either Benign or Malignant using Adaptive Neuro Fuzzy Inference System (ANFIS) classification approach. The proposed methodology for architectural distorted region detection is tested on the publicly available mammogram datasets Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) respectively. In this paper, the mammogram images from MIAS dataset are grouped into normal case (156 images), benign case (122 images) and malignant case (98 images). The mammogram images from DDSM dataset are grouped into normal case (144 images), benign case (112 images) and malignant case (145 images). The overall average detection rate of the proposed system on the mammogram images in MIAS dataset is about 98.7%. The overall average detection rate of the proposed system on the mammogram images in DDSM dataset is about 98.3%. The extensive simulations are carried out on the mammogram images which are obtained from these dataset and the results are compared with stated of art methods.

A hybrid end-to-end learning approach for breast cancer diagnosis: convolutional recurrent network

Skin lesions remain a significant global health issue, with their incidence rising steadily over the past few years. Early and accurate detection is crucial for effective treatment and improving patient outcomes. This work explores the integration of advanced Convolutional Neural Networks (CNNs) with Bidirectional Long Short Term Memory (BiLSTM) enhanced by spatial, channel, and temporal attention mechanisms to improve the classification of skin lesions. The hybrid model is trained to distinguish between various skin lesions with high precision. Among the models evaluated, the CNN (original architecture) with BiLSTM and attention mechanisms model achieved the highest performance, with an accuracy of 92.73%, precision of 92.84%, F1 score of 92.70%, recall of 92.73%, Jaccard Index (JAC) of 87.08%, Dice Coefficient (DIC) of 92.70%, and Matthews Correlation Coefficient (MCC) of 91.55%. The proposed model was compared to other configurations, including CNN with Gated Recurrent Units (GRU) and attention mechanisms, CNN with LSTM and attention mechanisms, CNN with BiGRU and attention mechanisms, CNN with BiLSTM, CNN with LSTM, CNN with BiGRU, CNN with GRU, standalone CNN, InceptionV3, Visual Geometry Group-16 (VGG16), and Xception, to highlight the efficacy of the proposed approach. This research aims to empower healthcare professionals by providing a robust diagnostic tool that enhances accuracy and supports proactive management strategies. The model’s ability to analyze high-resolution images and capture complex features of skin lesions promises significant advancements in early detection and personalized treatment. This work not only seeks to advance the technological capabilities in skin lesion diagnostics but also aims to mitigate the disease’s impact through timely interventions and improved healthcare outcomes, ultimately enhancing public health resilience on a global scale.

One of the most common cancers among women worldwide is breast cancer (BC), and early diagnosis can save lives. Early detection of BC increases the likelihood of a successful outcome by enabling treatment to start sooner. Even in areas without access to a specialist physician, machine learning (ML) aids in early BC detection. The medical imaging community is becoming more interested in using ML, and deep learning (DL) to increase the accuracy of cancer screening. Many disease-related data are sparse. However, for DL models to perform well, a large amount of data is required. Because of this, the DL models that are currently in use on medical images are not as effective as they could be. Convolutional neural network (CNN) models have recently gained popularity in the medical industry, and they perform admirably in terms of high performance and robustness at image classification. The proposed method classifies data using ensemble pre-trained models such as the dense convolutional network (DenseNet)-121 and EfficientNet-B5 feature extractor networks, as well as the support vector machine for classification. Using a modified meta-heuristic optimizer, the selected pre-trained CNN hyperparameters were optimized to improve the performance. The experimental results for the presented model on the INbreast dataset show that the EfficientNet-B5 model is effective for BC classification, with overall accuracy, sensitivity, specificity, precision, and area under the ROC curve (AUC) values of 99.9%, 99.9%, 99.8%, 99.1%, 1.0, respectively.

Because of its wide range of uses in computer vision applications, including image retrieval, remote sensing, object recognition, scene analysis, and surveillance, image classification has attracted a lot of attention. Assigning appropriate class labels to images according to their contents is the primary objective of image classification. In the domain of remote sensing, image classification and analysis play crucial roles in both military and civil applications. Conventional methods for scene analysis and remote sensing depended on low-level representations of features, such as those of color and texture. However, recent advancements have shifted towards the use of convolutional neural networks (CNNs), which have shown promising results in remote sensing and scene classification tasks. In light of effectiveness of deep learning (DL) models, this research aims to develop four DL models by fine tuning already existing DL models-CNNs, residual neural network (ResNet), visual geometry group (VGG-19), network mobile net V2 based model and classifies satellite images of RSI-CB256 data set in to four classes namely cloudy, desert, green_area and water. For the RSI-CB256 dataset, appropriate network structures are explored in this research to get good performance. The CNN, ResNet and VGG-19 base models achieved an accuracy of 90.48, 92.68 and 91.18 respectively. While the mobile net V2 based model outperformed the other three models with 96.83% accuracy.

In recent times, breast mass is the most diagnostic sign for early detection of breast cancer, where the precise segmentation of masses is important to reduce the mortality rate. This research proposes a new multiobjective optimization technique for segmenting the breast masses from the mammographic image. The proposed model includes three phases such as image collection, image denoising, and segmentation. Initially, the mammographic images are collected from two benchmark datasets like Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS). Next, image normalization and Contrast-Limited Adaptive Histogram Equalization (CLAHE) techniques are employed for enhancing the visual capability and contrast of the mammographic images. After image denoising, electromagnetism-like (EML) optimization technique is used for segmenting the noncancer and cancer portions from the mammogram image. The proposed EML technique includes the advantages like enhanced robustness to hold the image details and adaptive to local context. Lastly, template matching is carried out after segmentation to detect the cancer regions, and then, the effectiveness of the proposed model is analysed in light of Jaccard coefficient, dice coefficient, specificity, sensitivity, and accuracy. Hence, the proposed model averagely achieved 92.3% of sensitivity, 99.21% of specificity, and 98.68% of accuracy on DDSM dataset, and the proposed model averagely achieved 92.11% of sensitivity, 99.45% of specificity, and 98.93% of accuracy on MIAS dataset.

Noise and artefacts in mammogram images can obscure important indicators of microcalcifications, complicating accurate diagnosis. While traditional spatial filters can reduce noise and are effective to some extent, they often fail to enhance features crucial for classification. This study uses persistent homology (PH) to evaluate and improve the classification performance of various spatial filters on mammogram images. The evaluation process involves converting filtered images into persistence diagrams (PDs) to capture topological features. These diagrams are then vectorised into PH features for classification using a neural network classifier. This study also examines further filtering of PDs from filtered images to enhance classification performance. Using the Digital Database for Screening Mammography (DDSM) and Mammographic Image Analysis Society (MIAS) datasets, we evaluate Median, Wiener, Gaussian, and Bilateral filters alone and integrate them with PH-based filtering. Results show significant classification improvements, with Wiener filters achieving 96.33% accuracy on the DDSM dataset (up from 57.38%) and Gaussian filters reaching 85.33% on the MIAS dataset (up from 73.33%). These findings demonstrate the potential of PH-based filters to enhance diagnostic accuracy in breast cancer detection by refining topological features and effectively reducing noise.

Breast cancer remains one of the most prevalent and life-threatening diseases affecting women worldwide. Early detection is crucial for improving survival rates and treatment outcomes. This study proposes an advanced feature extraction method for classifying mammogram masses by combining multi-scale multi-orientation (MSMO) Gabor wavelets and gray-level co-occurrence matrix (GLCM) statistical features. MSMO Gabor filters extract detailed texture information across multiple scales and orientations, while GLCM captures statistical spatial relationships between pixel intensities. A feature selection process refines these features, enhancing classification accuracy. Experiments using Mammographic Image Analysis Society (MIAS) and Digital Database for Screening Mammography (DDSM) datasets validate the approach with machine learning classifiers, including random forest (RF), decision tree (DT), support vector machine (SVM), k-nearest neighbors (k-NN), and deep neural network (DNN). RF outperformed other models and achieved 96.64% accuracy on MIAS dataset and 95.90% on DDSM dataset. Our approach shows the efficacy of optimally combining MSMO Gabor and GLCM features to advance computer-aided diagnosis systems for early and precise breast cancer detection.