Comparative study and analysis on skin cancer detection using machine learning and deep learning algorithms

Exposure to UV rays due to global warming can lead to sunburn and skin damage, ultimately resulting in skin cancer. Early prediction of this type of cancer is crucial. A detailed review in this paper explores various algorithms, including machine learning (ML) techniques as well as deep learning (DL) techniques. While deep learning strategies, particularly CNNs, are commonly employed for skin cancer identification and classification, there is also some usage of machine learning and hybrid approaches. These techniques have proven to be effective classifiers of skin lesions, offering promising results for early detection. The paper analyzes various researchers’ reviews on skin cancer diagnosis to identify a suitable methodology for improving diagnostic accuracy. A publicly available dataset of dermoscopic images retrieved from the ISIC archive has been trained and evaluated. Performance analysis is done, considering metrics such as test and validation accuracy. The results indicate that the RF(random forest) algorithm outperforms other machine learning algorithms in both scenarios, with accuracies of 58.57% without augmentation and 87.32% with augmentation. MobileNetv2, ensemble of Dense Net and Inceptionv3 exhibit superior performance. During training without augmentation, MobileNetv2 achieves an accuracy of 88.81%, while the ensemble model achieves an accuracy of 88.80%. With augmentation techniques applied, the accuracies improved to 97.58% and 97.50%, respectively. Furthermore, experiment with a customized convolutional neural network (CNN) model was also conducted, varying the number of layers and applying various hyperparameter tuning methodologies. Suitable architectures, including a CNN with 7 layers and batch normalization, a CNN with 5 layers, and a CNN with 3 layers were identified. These models achieved accuracies of 77.92%, 97.72%, and 98.02% on the raw data and augmentation datasets, respectively. The experimental results suggest that these techniques hold promise for integration into clinical settings, and further research and validation are necessary. The results highlight the effectiveness of transfer learning models, in achieving high accuracy rates. The findings support the future adoption of these techniques in clinical practice, pending further research and validation.