Efficient Gastrointestinal Disease Classification Using Pretrained Deep Convolutional Neural Network

Gastrointestinal (GI) tract diseases are on the rise in world. These can have fatal consequences if not diagnosed initial stages. WCE (wireless capsule endoscopy) is advanced technology used to inspect gastrointestinal such as ulcerative-colitis, polyps, esophagitis, and ulcers. produces thousands of frames for a single patient’s procedure which manual examination tiresome, time-consuming, prone error; therefore, an automated needed. images suffer from low contrast increases inter-class intra-class similarity reduces anticipated performance. In this paper, efficient GI disease classification technique proposed utilizes optimized brightness-controlled contrast-enhancement method improve images. The applies genetic algorithm (GA) adjusting values brightness within image by modifying fitness function, improves overall quality This improvement reported using qualitative measures, peak signal noise ratio (PSNR), mean square error (MSE), visual information fidelity (VIF), index (SI), (IQI). As second step, data augmentation performed applying multiple transformations, then, transfer learning fine-tune modified pre-trained model Finally, disease, extracted features passed through machine-learning classifiers. To show efficacy performance, results original dataset well contrast-enhanced dataset. 15.26% accuracy, 13.3% precision, 16.77% recall rate, 15.18% F-measure. comparison with existing techniques shows that framework outperforms state-of-the-art techniques.