Enhanced Colon Cancer Detection and Classification with Deep Learning Techniques: A Comparative Analysis

Towards Interpretable Deep Learning for Colon Cancer Diagnosis in Healthcare System: A Comprehensive Review

Colon cancer is a life-threatening type of cancer, with a survival rate that is too low. This type of cancer is difficult to diagnose at an early stage due to slow and hidden growth. Blood in stools, abdominal pain, and chronic diarrhoea are indicators of colon cancer. Colonoscopy is utilised to detect colon-rectal cancer and polyps, which are prone to human error. This paper reviews recent studies on detecting colon cancer with the integration of deep learning and machine learning in healthcare. Histopathological images are fed into Mobile-Net, Two Stage CNN, Compact CNN Models, and Modified VGG-CNN+COATI OPTIMIZATION algorithm to extract features of affected cells. The literature available currently focuses on obtaining accuracy, sensitivity, efficiency, balanced performance, and better prediction with the aid of deep architecture. However, the existing work demonstrates better performance; it lacks interpretability, transparency(black-box issue), architectural simplicity and real-time validation. This paper describes how Machine learning, Deep learning, and hybrid methods have evolved in diagnosing colorectal cancer. Currently, explainable AI has emerged to interpret and diagnose colorectal tumours in healthcare, which would be a solution for the black box issue that will build trust and support, and better decision making as a future scope.

Deep Learning and CAD Systems in Breast Cancer Detection A Systematic Review of Diagnostic Accuracy and Clinical Integration

Breast cancer continues to be a significant health challenge for women worldwide, and early detection is essential for improving survival outcomes. Over the years, Computer-Aided Diagnosis (CAD) systems have supported radiologists by highlighting suspicious regions in mammograms, ultrasound images, and magnetic resonance imaging (MRI). However, traditional CAD systems evaluate image features based on predefined rules and are associated with high false-positive rates, limiting their ability to adapt to new or complex cases. This study aimed to examine the role of deep learning (DL) in the development of computer-aided diagnosis (CAD) systems and to evaluate the diagnostic accuracy and clinical integration of AI-based approaches in breast cancer detection. A systematic review was conducted by analyzing 13 studies published between 2000 and 2024. Relevant studies were identified through a comprehensive search of the PubMed, IEEE Xplore, and Scopus databases. The included studies were evaluated according to their methodology, deep learning architectures including Convolutional Neural Networks (CNNs), ResNet, EfficientNet, and Vision Transformers and their reported diagnostic performance in terms of accuracy, sensitivity, specificity, and false-negative reduction. The findings were synthesized narratively to compare the advantages and limitations of AI-based diagnostic systems with traditional CAD systems. The findings demonstrate that DL-based models outperform traditional CAD systems. These models contribute to earlier diagnosis by detecting subtle lesions, microcalcifications, and asymmetrical structures that may be overlooked by the human eye. However, several challenges remain, including the availability of large, high-quality datasets, the interpretability of AI decisions, ethical considerations, and integration into routine clinical practice. Deep learning-enhanced CAD systems represent a promising advancement in breast cancer diagnosis by improving diagnostic performance and supporting clinical decision-making. However, their successful implementation depends on high-quality data, transparent and interpretable models, and appropriate ethical and regulatory frameworks to ensure that AI serves as a supportive tool that complements, rather than replaces, radiologists.

A Hybrid Deep Learning Model for Colorectal Polyp Detection and Classification From Endoscopic Images

Globally, colorectal cancer (CRC) remains a key contributor to cancer‐related death, with most malignancies developing through the progression of colorectal polyps. Early detection and accurate histological classification of polyps during colonoscopy are essential for effective CRC screening and prevention. However, conventional colonoscopy may fail to detect certain lesions and exhibits variability in diagnostic performance due to operator dependence and challenging imaging conditions. To address these limitations, this study proposes a hybrid deep learning (DL) model that integrates YOLOv10 for polyp detection and feature extraction with a customized Convolutional Neural Network (CNN) for the histological classification of colorectal polyps into hyperplastic and adenomatous categories. A total of 6000 endoscopic images obtained from the Harvard Dataverse PolypsSet repository were used for model development and evaluation. To improve robustness and generalization, data augmentation techniques were applied during training, and stratified 5‐fold cross‐validation was employed to prevent data leakage between training and validation sets. Experimental results demonstrated that the proposed YOLOv10–CNN model achieved an average detection mAP@50 of 0.9848 and a classification accuracy of 0.9913 across the cross‐validation folds. External validation on an independent dataset achieved mAP@50 of 0.926, indicating good generalization ability to unseen data. Furthermore, the model achieved an inference speed of approximately 120 frames per second (FPS), demonstrating efficient computational performance. A web‐based graphical user interface was also developed to facilitate visualization of detection and classification results from colonoscopy videos. The findings suggest that the proposed hybrid model provides accurate and efficient polyp detection and classification while maintaining stable performance across internal and external evaluations. The proposed approach may serve as a supporting computer‐aided analysis tool for colorectal polyp screening.

Lung Cancer Detection from Histopathological Images using Custom CNN With XAI Techniques

One of the leading causes of cancer-related mortality worldwide is lung cancer. Improving patient survival requires early and precise diagnosis. Recent developments in deep learning make it possible to analyse histopathological lung pictures automatically. Because most deep learning models are opaque and considered black-box systems, their application in clinical practice is constrained. An explainable deep-learning framework for the categorisation of lung cancer is presented in this article. Gradient-weighted Class Activation Mapping (Grad-CAM) visualisation and a custom Convolutional Neural Network are also used. Three classes of histopathological lung tissue images: lung squamous cell carcinoma, lung adenocarcinoma, and normal tissue. Images are scaled to 224*224 pixels and preprocessed using data augmentation and normalisation techniques to enhance generalisation before training. With a test accuracy of 96.62%, the suggested Custom CNN is comparable to existing deep learning models like DenseNet121 (96.6%) and other CNN-based methods documented in earlier research. The suggested approach uses Grad-CAM to provide visual interpretability alongside classification performance.

Deep Learning Methods for Breast Cancer Detection, Classification, and Segmentation Using MRI Scans: A Systematic Review

Breast cancer remains one of the leading causes of cancer-related mortality among women worldwide, underscoring the importance of early and accurate diagnosis to improve patient outcomes. Magnetic resonance imaging (MRI) is a highly sensitive imaging modality for detecting breast malignancies, particularly in patients with dense breast tissue or those at high risk, where conventional imaging techniques may have limited sensitivity. Recent advances in deep learning (DL) have demonstrated considerable potential for improving the automated analysis of breast MRI, including tumour classification, prediction, and segmentation. This systematic review synthesises peer-reviewed studies published between 2014 and 2025 that exclusively applied DL techniques to breast MRI for cancer classification, prediction, or segmentation. The included studies were critically evaluated with respect to model architectures, dataset characteristics, image preprocessing methods, validation strategies, and reported performance metrics. The reviewed literature demonstrates that DL models consistently achieve high diagnostic performance and have the potential to enhance radiological workflows by supporting automated lesion detection and clinical decision-making. However, several challenges continue to limit their translation into routine clinical practice, including limited access to large, diverse, and well-annotated datasets, inadequate external validation, variability in MRI acquisition protocols, and concerns regarding model interpretability and generalisability. Future research should prioritise the development of robust, explainable, and clinically validated DL models trained on multicentre datasets using standardised evaluation frameworks. Addressing these challenges will be essential to improve the reliability, reproducibility, and clinical applicability of AI-assisted breast cancer diagnosis using MRI.

Deep Learning-Based Automated Skin Cancer Detection Using Dermoscopic Images