176 pp. £18.95 (paper) C.R. Lamb Diagnostic Imaging of the Dog and Cat 1994 Wolfe Publishers London 0-7234-1933-7

Abstract

Skin cancer is one of the most prevalent and deadly types of cancer. Dermatologists diagnose this disease primarily visually. Multiclass skin cancer classification is challenging due to the fine-grained variability in the appearance of its various diagnostic categories. On the other hand, recent studies have demonstrated that convolutional neural networks outperform dermatologists in multiclass skin cancer classification. We developed a preprocessing image pipeline for this work. We removed hairs from the images, augmented the dataset, and resized the imageries to meet the requirements of each model. By performing transfer learning on pre-trained ImageNet weights and fine-tuning the Convolutional Neural Networks, we trained the EfficientNets B0-B7 on the HAM10000 dataset. We evaluated the performance of all EfficientNet variants on this imbalanced multiclass classification task using metrics such as Precision, Recall, Accuracy, F1 Score, and Confusion Matrices to determine the effect of transfer learning with fine-tuning. This article presents the classification scores for each class as Confusion Matrices for all eight models. Our best model, the EfficientNet B4, achieved an F1 Score of 87 percent and a Top-1 Accuracy of 87.91 percent. We evaluated EfficientNet classifiers using metrics that take the high-class imbalance into account. Our findings indicate that increased model complexity does not always imply improved classification performance. The best performance arose with intermediate complexity models, such as EfficientNet B4 and B5. The high classification scores resulted from many factors such as resolution scaling, data enhancement, noise removal, successful transfer learning of ImageNet weights, and fine-tuning [70], [71], [72]. Another discovery was that certain classes of skin cancer worked better at generalization than others using Confusion Matrices.