Ulcerative Colitis Detection and Severity Prediction Using a Hybrid Deep Learning Model

A Convolutional Neural Network Classifies Intestinal Diseases Using Endoscopic Images

This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.

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.

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

Colon cancer represents a growing universal issue related to health, with prompt and accurate detection essential for indispensable to improving outcomes of people's health. Standard approaches, like colonoscopy and histopathology, while useful, tend to be invasive, time-intensive and prone to human interpretative bias. Recent developments in deep learning (DL) have facilitated the creation of automated systems that improve the precision, speed and uniformity of colon cancer diagnosis and categorisation. This paper offers a thorough comparative examination of various advanced DL models, including ResNet, DenseNet and MobileNet, applied to multi-modal imaging datasets consisting of colonoscopy images. Quantitative findings indicate exceptional accuracy, precision and recall in diagnostic tasks, with MobileNet DL models outperforming in tumour diagnosis and grading than other peer groups.

Skin Disease Detection Using Deep Learning

Early and accurate diagnosis of skin diseases is essential for effective treatment and improved patient outcomes. This paper presents SkinScan AI, a deep learning-based framework for automated skin disease detection and classification from dermoscopic images using the HAM10000 benchmark dataset. The proposed DenseNet121 model classifies skin lesions into seven clinically significant categories: Actinic Keratoses (akiec), Basal Cell Carcinoma (bcc), Benign Keratosis-like Lesions (bkl), Dermatofibroma (df), Melanoma (mel), Melanocytic Nevi (nv), and Vascular Lesions (vasc). To address dataset imbalance, the framework incorporates MixUp augmentation, focal loss with label smoothing, minority-class weighting, and oversampling. A two-stage fine-tuning strategy with Test-Time Augmentation (TTA) improves model generalization and prediction robustness. Comparative evaluation with a custom CNN baseline and EfficientNetB3 demonstrates that DenseNet121 achieves the best performance, obtaining 81.82% TTA accuracy, a weighted F1-score of 0.8096, and an AUC of 0.9441. Grad-CAM provides visual explanations by highlighting lesion-relevant regions, improving model interpretability and increasing user confidence in the prediction process. The trained model is deployed as a Flask-based web application with secure authentication, prediction history, severity assessment, medication guidance, doctor referral recommendations, and downloadable PDF reports. Experimental results demonstrate that the proposed framework effectively handles class imbalance while providing accurate, interpretable, and practical decision support for computer-aided skin disease diagnosis. The system offers a scalable and user-friendly solution that can assist healthcare professionals and improve access to reliable preliminary skin disease screening.

Detection and Classification of Colorectal Cancer Types Using Deep Residual Learning based on ResNet-50 with Adam Optimization Method

AUTOMATED SKIN DISEASE CLASSIFICATION USING HYBRID DEEP LEARNING AND ENSEMBLE LEARNING TECHNIQUES

Skin diseases impact millions of people globally, from prevalent issues such as eczema and acne to life-threatening conditions such as melanoma and psoriasis. Due to the similarity in visual features of these diseases, extensive variation in skin types and dependence on subjective clinical observations, diagnosing these diseases timely and accurately is a challenging task. Conventional diagnostic techniques, such as manual palpation and biopsy, are invasive, time consuming, and subjective. To overcome the drawbacks, in this paper we present a hybrid architecture that combines deep learning-based feature extraction and ensemble machine learning classifiers for automated classification of skin diseases. We use a state-of-the-art convolutional neural network, i.e., EfficientNet to obtain hierarchical representations of breast cancer images which incorporate local and global patterns of lesions on skin. These deep features then are served as inputs to the ensemble classifiers including XGBoost, LightGBM based on multiple decision trees for better classification accuracy and avoiding over-fitting. The proposed system is tested on a publicly available dermatology dataset consisting multiple disease classes, resulting in the overall accuracy of 92.4%, which is higher compared to the conventional CNN, SVM and Random Forest techniques. Moreover, these results show high precision, recall, and F1-score in all classes (results not shown), suggesting that the proposed framework can perform well to discriminate visually similar diseases. The results show that integrating deep feature extraction and ensemble learning provides a robust, non-invasive, scalable solution for automated dermatological diagnosis and has the potential to be integrated into clinical decision support systems.