Endoscopic Image Classification Based on Explainable Deep Learning

Abstract

Deep learning has achieved remarkably positive results and impacts on medical diagnostics in recent years. Due to its use in several proposals, deep learning has reached sufficient accuracy to implement; however, the algorithms are black boxes that are hard to understand, and model decisions are often made without reason or explanation. To reduce this gap, explainable artificial intelligence (XAI) offers a huge opportunity to receive informed decision support from deep learning models and opens the black box of the method. We conducted an explainable deep learning method based on ResNet152 combined with Grad–CAM for endoscopy image classification. We used an open-source KVASIR dataset that consisted of a total of 8000 wireless capsule images. The heat map of the classification results and an efficient augmentation method achieved a high positive result with 98.28% training and 93.46% validation accuracy in terms of medical image classification.

Spatial-attention ConvMixer architecture for classification and detection of gastrointestinal diseases using the Kvasir dataset

“…They demonstrate that the model is capable of accurate and robust classification and that it is able to identify the relevant regions in the input images with high accuracy. These findings have important implications for the medical field, where accurate and reliable classification of medical images is critical for effective diagnosis and treatment [ 59 , 60 ].…”

Efficient-gastro: optimized EfficientNet model for the detection of gastrointestinal disorders using transfer learning and wireless capsule endoscopy images

“…An effective augmentation technique was employed to classify medical images using the heat map of classification results, which had an accuracy of 98.2% during training and 93.46% during validation ( Mukhtorov et al, 2023 ). The previous results on Gastrointestinal tracts demonstrate that the proposed model is outclassed in terms of all performance metrics; it achieved 99.22% accuracy on dataset 1 (eight classes) and 96.63% on dataset 2 (four classes).…”

“…They made use of the 8,000 wireless capsule images that were available for viewing in the freely available Kvasir database ( Khan et al, 2022 ). A high-performing outcome for the classification of medical images was achieved by using an efficient augmentation method in conjunction with the classification results, which had an accuracy of 98.28% during training and 93.46% during validation ( Mukhtorov et al, 2023 ). In a separate piece of research, the researchers explain the methodologies and processes for applying deep learning algorithms to examine a wide variety of gastrointestinal disorders and recognize these images.…”

Integrating Principal Component Analysis and Multi-Input Convolutional Neural Networks for Advanced Skin Lesion Cancer Classification

“…Convolutional neural networks (CNNs) have revolutionized image recognition tasks due to their ability to learn complex hierarchical features from images. In medical imaging, CNN architectures like EfficientNet [5], VGG-16 [6], ResNet [6], and GoogleNet [7] have been particularly effective. For example, EfficientNet has been utilized for its efficiency and scalability in processing high-resolution medical images, while ResNet's deep residual learning framework helps in learning from enormous datasets commonly used in medical diagnostics.…”