Grad-CAM Visualization and Ensemble Learning for Improved Gastrointestinal Disease Classification Using CNNs

The aim of this paper is to investigate the efficacy of ensemble learning techniques in convolutional neural network (CNN) models for gastrointestinal disease classification using the Kvasir v2 dataset [1]. A particular emphasis is placed on visualizing and comprehending model predictions through Gradient-weighted Class Activation Mapping (Grad-CAM). Eleven pretrained CNN models such as VGG16, ResNet50, NASNetMobile, NASNetLarge, MobileNetV3Large, InceptionV3, InceptionResNetV2, EfficientNetB4, DenseNet201, DenseNet121, and VGG19 were fine-tuned on the Kvasir v2 dataset. These models were assessed based on accuracy, precision, recall, F1 score, and specificity. The top-performing models were further combined using ensemble methods, including hard voting and weighted averaging, to improve classification performance. Grad-CAM was employed to produce visual explanations of model predictions, enabling a detailed analysis of the impact of different ensemble combinations on the decision-making process. This study demonstrates that ensemble learning can enhance model performance and provides valuable insights into the interpretability of CNN models in medical image analysis.