Feb 2026· Digital Health· Vol 12· 0 citations· 132 references
Medicine
TL;DR
A systematic review of recent notable studies of CNN-based colon cancer classification using histopathological WSI showed that Res-Net architecture demonstrated the highest accuracy in both bi- and multi-classification tasks over the past five years.
Abstract
Introduction Recent studies indicated the advancement of Convolutional Neural Networks (CNNs) have facilitated the colon cancer diagnosis process via medical image processing and multi-modality prediction analysis. This paper aims to systematically review the recent notable studies of CNN-based colon cancer classification using histopathological WSI and compare their strengths and limitations as inspirations for future research directions. Method This systematic review was conducted under a PRISMA analysis framework. The PICO selection and PROBAST bias assessment tools were used in data selection and validation. The systematic review compared accuracy from notable studies in benchmark architectures (Res-Net, Inception, VGG-Net, Dense-Net, and Efficient-Net) regarding the bi- and multi-classification tasks (2020-2025). Results The result showed that Res-Net architecture demonstrated the highest accuracy in both bi- (99.97%) and multi-classification (99.96%) tasks over the past five years. Newer architectures (Efficient-Net, Dense-Net) outperformed older models (VGG-Net, Res-Net) by optimizing depth and feature reuse while minimizing biases. Low computational model were more suitable for real-world deployment and clinical interaction. Conclusion This review contributes to the systematic synthesis of knowledge regarding advancements in CNN-based colon cancer classification. Also, this paper provided future research guidelines for further directions (quantum AI, advanced analytics, and lightweight integrations) and implementation in clinical settings.
These days, digital pathology plays a crucial role in tumor detection and prognosis, particularly in objectively evaluating histological images for colorectal cancer (CRC). Deep learning models have demonstrated significant success in image classification, prompting researchers to adopt these methods for medical image analysis. This research explores deep learning methods, specifically using ResNet architectures (ResNet-18, ResNet-50, ResNet-101), combined with various optimization methods, including Adam, Stochastic Gradient Descent with Momentum (SGDM), and Root Mean Square Propagation (RMSProp), for classifying colorectal cancer types from histological images. A comprehensive evaluation was conducted to identify the most effective neural network architecture and optimization approach. Among the tested combinations, the ResNet-50 model with the Adam optimization method achieved the highest accuracy of 99.86% on the NCT-CRC-HE-7K dataset and an accuracy of 98.36% on the NCT-CRC-HE-100K dataset, outperforming other tested combinations and existing approaches in the literature, effectively differentiating between benign and aggressive colorectal cancer. Lastly, the suggested model was compared to the current models, which were assessed to ascertain the most effective neural network models and the best training approach for our case study on colon tumor segmentation.
H. K. Omer, Salwa M. Hasan, Lozan M. Abdullrahman et al.· passer of basic and applied...· 0 citations
Breast cancer is currently one of the leading malignancies and mortality rates among women globally, creating an urgent need for accurate and efficient automated diagnostic tools. This study proposes and systematically compares convolutional neural network (CNN) architectures, including VG16, ResNet18, ResNet50, DenseNet121, EfficientNet-B0, and Swin-Transformer, applied to whole slide image (WSI) histopathology data for breast cancer prediction and classification. The models were trained and evaluated on the same WSI dataset with consistent image preprocessing techniques, allowing for objective comparison of performance. Experimental results showed that the performance of the models varied depending on the training strategy. In the non-fine-tune setting, ResNet18 achieved the best results with a C-index of 0.6587 and a Mean time-dependent AUC of 0.7317. When performing fine-tuning, ResNet50 outperformed the other architectures with a C-index of 0.6877 and a Mean AUC of 0.7738. Meanwhile, in the non-fine-tuned setup with combined clinical testing, Swin-Transformer achieved the highest performance with a C-index of 0.6793 and a Mean AUC of 0.7619.
Cuu-Duong Dang, T. Le, An-Thai Vo et al.· 2026 International Workshop...· 0 citations
Introduction: Lung cancer remains one of the leading causes of cancer-related mortality worldwide. Despite advances in diagnostic imaging, histopathological assessment continues to be the gold standard for accurate classification of lung cancer subtypes. However, manual interpretation of histopathological slides is time-consuming and susceptible to inter-observer variability, underscoring the need for efficient Computer-Aided Diagnostic (CAD) systems.
Aim: To develop a Convolutional Neural Network (CNN)- based model for automated classification of lung carcinoma histopathological images by integrating classical gradientbased edge detection techniques to enhance image quality and improve classification performance.
Materials and Methods: This retrospective analytical study was conducted in the ICMR-funded laboratory at Brainware University, Kolkata, West Bengal, India. The project commenced in February 2024 and is proposed to conclude in February 2027. To date, it has yielded a dataset of 11,580 histopathological images of lung tissue, categorised as Lung Adenocarcinoma (LUAD), Lung Squamous Cell Carcinoma (LSCC), and Non Malignant (BNM). The present work represents a split study and forms part of a larger ICMR-funded project spanning three years. This component focuses on LUAD, LSCC, and non-malignant lung tissue, thereby encompassing the two most common Non Small Cell Lung Cancer (NSCLC) subtypes (which together account for approximately 85% of lung cancers) along with a normal reference class. Edge enhancement was performed using Sobel and Scharr operators to emphasise structural boundaries. The processed images were used to train a custom-designed CNN architecture comprising three convolutional blocks, each followed by pooling layers, and fully connected layers for classification. The model was trained for 100 epochs with a batch size of 32 and evaluated using accuracy, precision, sensitivity, specificity, and F1-score as performance metrics.
Results: Sobel-based preprocessing yielded superior model performance compared with the Scharr method. The proposed CNN trained on Sobel-enhanced images achieved an overall classification accuracy of 93.55%, outperforming the 81.33% accuracy obtained with Scharr-based preprocessing. The Sobelenhanced model also demonstrated stronger generalisation, with AUC values of 0.98, 1.00, and 0.98 for the LUAD, BNM, and LSCC classes, respectively.
Conclusion: The integration of edge detection with CNN-based analysis effectively enhances the classification of lung cancer histopathological images. The Sobel operator notably improved model accuracy, precision, and generalisation, suggesting its potential suitability for highlighting morphological boundaries in tissue sections. Future work will extend this framework by exploring additional edge detection methods, implementing stain normalisation, and validating performance on external datasets to further optimise model robustness and clinical applicability.
Saurav Mali, Ankur Mukherjee, Biren Parikh et al.· Journal of Clinical and Diag...· 0 citations
This research is motivated by the high urgency of early diagnosis in bone cancer cases to reduce patient mortality rates. This study comparatively analyzes the performance of the Convolutional Neural Network (CNN) algorithm with ResNet50 architecture, Support Vector Machine (SVM), and their integration in a Hybrid CNN-SVM model for medical image classification. The research methodology involved a dataset of 8,814 radiological images processed through normalization and augmentation stages. In single-model testing, the end-to-end ResNet50 architecture achieved an accuracy of 87%, but showed limitations in generalizing microscopic textures at the softmax classification layer. On the other hand, the SVM algorithm supported by manual Histogram of Oriented Gradients (HOG) feature extraction demonstrated significant stability with an accuracy of 93.58%, proving the superiority of the optimal margin method in handling specific feature dimensions in medical images. The crucial finding in this study shows that the Hybrid CNN-SVM model—which utilizes ResNet50 as an automatic feature extractor and SVM as the final classifier—achieved peak performance with an accuracy of 95.18%, Precision value of 0.98, Recall of 0.96, and AUC of 0.98. These results confirm that the synergy between CNN hierarchical feature extraction and SVM classification robustness can significantly minimize the risk of false negatives, making it highly recommended as a reliable Computer-Aided Diagnosis (CAD) instrument to assist medical practitioners in early detection of bone cancer.
B. Wijaya, Edoart Joel Pardede, Muhammad Reza et al.· JIKO (Jurnal Informatika dan...· 0 citations
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.
Şeyma Betül Samsa, Hina Zahoor· İstanbul Gelişim Üniversites...· 0 citations
This study provides a clear and actionable framework to bridge the gap between DL‐based segmentation research and clinical deployment, and proposes a roadmap for future research focusing on lightweight architectures, edge–cloud integration, federated learning and explainable AI (XAI).
Muhammad Sufyan, Jun Qian, Jianqiang Li et al.· Expert systems· 0 citations
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