Aug 2026· Jurnal Teknologi Informatika dan Komputer· Vol 12, pp. 972-983· 0 citations
TL;DR
This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.
Abstract
Intestinal diseases such as polyps, esophagitis, and ulcerative colitis may lead to serious complications when they are not detected early. Endoscopy provides important visual information for diagnosis; however, manual interpretation still depends on clinical expertise and requires time. This study aimed to develop an automatic intestinal disease classification model based on Convolutional Neural Network (CNN). The study used a Research and Development method consisting of data collection, preprocessing, model training, evaluation, and web interface implementation. The dataset was obtained from Kaggle and consisted of 6,000 endoscopic images categorized into normal, polyps, esophagitis, and ulcerative colitis. Each class contained 1,500 images, divided into approximately 87% training data and 13% testing data. The model was trained for 15 epochs and evaluated using accuracy, loss, confusion matrix, and single-image testing. The results showed stable validation accuracy in the range of 98-99%, while single-image testing produced confidence scores from 99.91% to 100%. This system is recommended as an initial endoscopic image classification aid, with further development involving a random-image class and an examination history database.
The proposed framework establishes a reliable and lightweight baseline for automated gastrointestinal disease detection and demonstrates that ConvNeXt-Tiny effectively captures disease-relevant visual patterns in endoscopic images while maintaining consistent performance across varying training conditions.
Muhammad Faqih, O. Q. Aziz, Ajib Hanani· Jurnal Ilmu Komputer dan Inf...· 0 citations
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.
Yao-Tien Chen, Debalke Embeyale Sahilu· International journal of ima...· 0 citations
Early diagnosis of ear diseases is essential to prevent serious complications such as chronic infections and permanent hearing loss. Conventional otoscopic diagnosis relies heavily on experienced clinicians and specialized equipment, which limits healthcare accessibility in rural and underserved regions. This paper presents an automated ear disease detection system based on Convolutional Neural Networks (CNNs) for binary classification of otoscopic images into normal and abnormal categories. The proposed framework incorporates image preprocessing and data augmentation techniques to improve image quality, increase dataset diversity, and enhance the robustness of the classification model. The model was trained and evaluated using a publicly available dataset containing 1,370 otoscopic images. Experimental results demonstrate an overall classification accuracy of 91.2%, with a precision of 90.4%, a recall of 89.8%, and an F1-score of 90.1%. The proposed system effectively distinguishes normal ear conditions from abnormalities, including Acute Otitis Media (AOM) and other infectious ear diseases. Furthermore, the trained CNN model was integrated into a Flask-based web application to provide an accessible, real-time diagnostic support tool for primary healthcare settings. The proposed solution has the potential to assist clinicians in early screening, improve diagnostic efficiency, and enhance healthcare accessibility in resource-limited environments.
Rakshitha N Poojary, Raksha V Shetty· 2026 International Conferenc...· 0 citations
This proposed model uniquely combines- Local convolutional features via ResNet-50, Global contextual features via Vision Transformer, and Handcrafted clinical texture descriptors (GLCM + LBP) and addresses the core limitations of single-architecture models that tend to either underfit local texture patterns or miss long-range spatial dependencies.
S. Dhole, C. More, Anuradha S. Nigade et al.· International Journal of Ele...· 0 citations
Skin disease classification through deep learning has emerged as an important research field because of its ability to facilitate the accurate and timely medical diagnosis. A hybrid deep learning model combining Convolutional Neural Networks (CNN) and a Vision Transformer (ViT) is introduced in the present study to classify skin diseases automatically from dermoscopic images. Images selected are dermoscopic for their ability to provide detailed information about the boundary, distribution of pigmentation, and texture characteristics of lesions, key elements for accurate diagnosis. The experimental dataset is made up of 2000 dermoscopic images divided into 1400 images for model training and 600 images for testing purposes. The proposed framework is compared with the standalone CNN model, with the same experimental settings. Balanced sampling is combined with data augmentation techniques in order to reduce the impact of class imbalance within different categories of skin diseases during the training procedure. Common metrics used to evaluate the effectiveness of models are accuracy, precision, recall and F1-score. The experimental results have shown that the proposed Hybrid CNN–ViT model achieves 91.7% accuracy, which is higher than 84.1% accuracy obtained by the standalone CNN model. CNN-based local feature extraction is combined with the global contextual representation ability of the Vision Transformer, achieving the improved performance is due to the feature fusion between the two, which enables the model to have a better discrimination of visually similar skin lesions. The integration constitutes the main novelty of the proposed work. Moreover, the model is tested with the full test data to obtain the constant and reliable evaluation of the model. The hybrid architecture consumes more computational resources than a conventional CNN model, but the results show that it can achieve better accuracy in skin disease classification, strength, and reliability when detecting skin diseases automatically.
Namasivayam M, V. S, R. R et al.· International Conference Com...· 0 citations
Comparative evaluations revealed that pre-processed images significantly improved classification consistency and accuracy, highlighting the benefits of color normalization techniques and Particle Swarm Optimization was employed for hyperparameter tuning.
Subrata Sinha, Saurav Mali, S. Rajkhowa et al.· Scientific Reports· 0 citations
