Skin Lesion Detection and Classification Using Convolutional Neural Network for Deep Feature Extraction and Support Vector Machine

Abstract

Pigmented skin lesion identification is essential for detecting harmful pathologies related to this large organ, especially cancer. An analysis of the different methods and projects developed to diagnose these illnesses throughout the years showed that they had become very useful tools to identify melanoma, dermatofibroma, and basal cell carcinoma, among other types of cancer, are seen through the use of new computer-aided technologies. The most common diagnosis is based on dermoscopy and the dermatologist expertise that can improve accuracy with image detection techniques and classification by computer. Therefore, this study aims to develop software models able to detect and classify skin cancer. The following work is based on the use of dermoscopy images obtained from the HAM10000 dataset, a database with 10000 images previously tested and validated for research use. The main process is divided into three relevant parts: image segmentation, feature extraction (FE) using ten different pre-trained Convolutional Neural Networks (CNNs), and Support Vector Machine (SVM) to establish a classification model. According to the results, the models of classification performed very well using the image segmentation step, showing average accuracies between 80.67% (Xception) and 90% (Alexnet). In contrast to the process without using image segmentation, where no method reached 60%. AlexNet plus SVM model showed the minor running time and presented the higher accuracy rate (90.34%) for the correct identification and classification of the seven categories of cutaneous lesions taken into account.

A novel framework of multiclass skin lesion recognition from dermoscopic images using deep learning and explainable AI

“…a) Consider the image datasets a = { x 1 , x 2 , … …, x n } ( 40 ), where x n ϵ UF is a representative image from the dataset. Let x n have a total of D k pixels; the homogeneous pixel matrix coordinates D k or X n are as follows:…”

“…The accuracy of the experiments on the ISBI-2016, ISBI-2017, and PH2 datasets was 95.1%, 94.8%, and 98.4%, respectively. Utilizing the HAM10000 dataset, the authors ( 40 ) built a classification model using 10 distinct pretrained CNNs and SVMs to extract the features. The model achieved an accuracy of 90.34%.…”

The effect of hair removal and filtering on melanoma detection: a comparative deep learning study with AlexNet CNN

“…Several researchers have proposed specific approaches for skin cancer classification using CNN and other deep-learning techniques. Among these approaches, Yanchatuña et al (2021) used a combination of CNNs and support vector machines (SVMs) to detect and classify skin cancer, obtaining an average accuracy between 80.67% and 90%, with an outstanding performance of 90.34% for the AlexNet plus SVM model. Popescu, El-Khatib & Ichim (2022) developed a skin lesion classification system involving multiple CNNs trained on the HAM10000 dataset, capable of predicting seven skin lesions, including melanoma.…”

Artificial intelligence based glaucoma and diabetic retinopathy detection using MATLAB — retrained AlexNet convolutional neural network

“…Artificial intelligence (AI), through deep learning methods, enables classification models with the capability to identify patterns within extensive image datasets for predictive analysis. [12][13][14][15] Consequently, employing AI for the automated analysis of fundus images can assist physicians by facilitating accessible, reliable, and affordable detection of glaucoma and other related visual pathologies (Table 1).…”