Detection of Skin Cancer Based on Skin Lesion Images Using Deep Learning

Abstract

An increasing number of genetic and metabolic anomalies have been determined to lead to cancer, generally fatal. Cancerous cells may spread to any body part, where they can be life-threatening. Skin cancer is one of the most common types of cancer, and its frequency is increasing worldwide. The main subtypes of skin cancer are squamous and basal cell carcinomas, and melanoma, which is clinically aggressive and responsible for most deaths. Therefore, skin cancer screening is necessary. One of the best methods to accurately and swiftly identify skin cancer is using deep learning (DL). In this research, the deep learning method convolution neural network (CNN) was used to detect the two primary types of tumors, malignant and benign, using the ISIC2018 dataset. This dataset comprises 3533 skin lesions, including benign, malignant, nonmelanocytic, and melanocytic tumors. Using ESRGAN, the photos were first retouched and improved. The photos were augmented, normalized, and resized during the preprocessing step. Skin lesion photos could be classified using a CNN method based on an aggregate of results obtained after many repetitions. Then, multiple transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were used for fine-tuning. In addition to experimenting with several models (the designed CNN, Resnet50, InceptionV3, and Inception Resnet), this study's innovation and contribution are the use of ESRGAN as a preprocessing step. Our designed model showed results comparable to the pretrained model. Simulations using the ISIC 2018 skin lesion dataset showed that the suggested strategy was successful. An 83.2% accuracy rate was achieved by the CNN, in comparison to the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models.

Keywords: ISIC 2018; computer vision; convolutional neural network; deep learning; machine learning; skin lesion.

Figure 1

Skin cancer cases globally (22 March 2022) [1].

Figure 2

Process of cancer detection.

Figure 3

Classes of ISIC2018 dataset.

Figure 4

Lesion images from ISIC2018 dataset.

Figure 5

Images after the enhancement process.

Figure 6

Output of the proposed image augmentation process.

Figure 7

An illustration of the skin cancer detection technique.

Figure 8

Distribution of dataset.

Figure 9

Best confusion matrix of CNN.

Figure 10

Best confusion matrix of InceptionV3.

Figure 11

ROC curve for CNN model.

Figure 12

ROC curve for InceptionV3.