Detection of Benign and Malignant Tumors in Skin Empowered with Transfer Learning

• Published in: Computational intelligence and neuroscience Vol. 2022; pp. 4826892 - 9
• Main Authors: Ghazal, Taher M., Hussain, Sajid, Khan, Muhammad Farhan, Khan, Muhammad Adnan, Said, Raed A. T., Ahmad, Munir
• Format: Journal Article
• Language: English
• Published: United States Hindawi 24.03.2022; John Wiley & Sons, Inc; Hindawi Limited
• Subjects: Accuracy; Algorithms; Artificial neural networks; Brain cancer; Cancer; Classification; Customization; Datasets; Deep learning; Humans; Likelihood ratio; Literature reviews; Machine Learning; Medical imaging; Melanoma; Neoplasms; Neural networks; Neural Networks, Computer; Parameter sensitivity; Physicians; Skin; Skin cancer; Skin diseases; Support vector machines; Transfer learning; Tumors; Pakistan
• ISSN: 1687-5265; 1687-5273
• DOI: 10.1155/2022/4826892

Skin cancer is a major type of cancer with rapidly increasing victims all over the world. It is very much important to detect skin cancer in the early stages. Computer-developed diagnosis systems helped the physicians to diagnose disease, which allows appropriate treatment and increases the survival ratio of patients. In the proposed system, the classification problem of skin disease is tackled. An automated and reliable system for the classification of malignant and benign tumors is developed. In this system, a customized pretrained Deep Convolutional Neural Network (DCNN) is implemented. The pretrained AlexNet model is customized by replacing the last layers according to the proposed system problem. The softmax layer is modified according to binary classification detection. The proposed system model is well trained on malignant and benign tumors skin cancer dataset of 1920 images, where each class contains 960 images. After good training, the proposed system model is validated on 480 images, where the size of images of each class is 240. The proposed system model is analyzed using the following parameters: accuracy, sensitivity, specificity, Positive Predicted Values (PPV), Negative Predicted Value (NPV), False Positive Ratio (FPR), False Negative Ratio (FNR), Likelihood Ratio Positive (LRP), and Likelihood Ratio Negative (LRN). The accuracy achieved through the proposed system model is 87.1%, which is higher than traditional methods of classification.