Comparative Analysis of Skin Cancer (Benign vs. Malignant) Detection Using Convolutional Neural Networks.

Mohammed Rakeibul Hasan,Atef Zaguia,Mohammad Monirujjaman Khan,Manjit Kaur,Mohammed Ishraaf Fatemi,Kalidoss Rajakani

doi:10.1155/2021/5895156

Abstract

We live in a world where people are suffering from many diseases. Cancer is the most threatening of them all. Among all the variants of cancer, skin cancer is spreading rapidly. It happens because of the abnormal growth of skin cells. The increase in ultraviolet radiation on the Earth's surface is also helping skin cancer spread in every corner of the world. Benign and malignant types are the most common skin cancers people suffer from. People go through expensive and time-consuming treatments to cure skin cancer but yet fail to lower the mortality rate. To reduce the mortality rate, early detection of skin cancer in its incipient phase is helpful. In today's world, deep learning is being used to detect diseases. The convolutional neural network (CNN) helps to find skin cancer through image classification more accurately. This research contains information about many CNN models and a comparison of their working processes for finding the best results. Pretrained models like VGG16, Support Vector Machine (SVM), ResNet50, and self-built models (sequential) are used to analyze the process of CNN models. These models work differently as there are variations in their layer numbers. Depending on their layers and work processes, some models work better than others. An image dataset of benign and malignant data has been taken from Kaggle. In this dataset, there are 6594 images of benign and malignant skin cancer. Using different approaches, we have gained accurate results for VGG16 (93.18%), SVM (83.48%), ResNet50 (84.39%), Sequential_Model_1 (74.24%), Sequential_Model_2 (77.00%), and Sequential_Model_3 (84.09%). This research compares these outcomes based on the model's work process. Our comparison includes model layer numbers, working process, and precision. The VGG16 model has given us the highest accuracy of 93.18%.

Highlights

According to the World Cancer Research Fund, among all cancers, skin cancer is the 19th most common
Some precancerous skin growths have a minimal chance of developing into cancer, whereas others have a very high chance. ere are many kinds of malignant skin growth, like melanoma, carcinoma, sarcoma, squamous cell carcinoma, and skin lymphoma [2]
The Convolutional Neural Network (CNN) is proposed in the systems of this study to detect skin cancer

Summary

Introduction

According to the World Cancer Research Fund, among all cancers, skin cancer is the 19th most common. Deep Learning, notably the Convolution Neural Network, can be used to identify skin cancer quickly and cheaply using image classification. It has become a lifesaver for poor people. Due to the advancement of deep learning in the medical science field, it has become much easier For this reason, the CNN is proposed in the systems of this study to detect skin cancer. A convolutional-deconvolutional architecture is used to segment the data Another group of researchers worked on the same dataset, but they used CNN based on symptomatic feature extraction [6]. Is research shows differentiation among the models and architectures of deep learning, focusing only on skin cancer.

Method and Methodology

16 Layers of VGG-16 Figure 7

Findings

Classes