Abstract
The convolutional neural network showed considerable success in medical imaging with explainable AI for cancer detection and recognition. However, the irrelevant and large number of features increases the computational time and decreases the accuracy. This work proposes a deep learning and fuzzy entropy slime mould algorithm-based architecture for multiclass skin lesion classification. In the first step, we employed the data augmentation technique to increase the training data and further utilized it for training two fine-tuned deep learning models such as Inception-ResNetV2 and NasNet Mobile. Then, we used transfer learning on augmented datasets to train both models and obtained two feature vectors from newly fine-tuned models. Later, we applied a fuzzy entropy slime mould algorithm on both vectors to get optimal features that are finally fused using the Serial-Threshold fusion technique and classified using several machine learning classifiers. Eventually, the explainable AI technique named Gradcam opted for the visualization of the lesion region. The experimental process was conducted on two datasets, such as HAM10000 and ISIC 2018, and achieved 97.1 and 90.2% accuracy, better than the other techniques.