A machine learning model for skin disease classification using convolution neural network

Abstract

Melanoma is a skin disease that tends to be lethal. It occurs when melanocytes develop in an uncontrolled manner. Melanoma goes under a few different names, including malignant melanoma. The incidence of melanoma is at its highest level ever recorded in both Australia and New Zealand. It is estimated that one in every 15 white New Zealanders will indeed be diagnosed with melanoma at some point in their lives. Aggressive malignancy was the third most common kind of cancer in men and women in 2012, respectively. Melanoma can develop at any age in adults, but it is highly unusual in children and teenagers. It is hypothesized that the first step in developing melanoma is an unregulated multiplication of melanocytic stem cells that have been genetically altered. The survival rate can significantly increase if melanoma is identified in dermos copy images at an earlier stage. On the other hand, the detection of melanomas is an incredibly challenging task. Consequently, the detection and recognition of skin cancer are of tremendous assistance to the accuracy of pathologists. In this research, a deep learning technique is shown for reliably diagnosing the type of melanoma present at a preliminary phase. The proposed model makes a distinction among lesion maligna, superficial spreading, and nodular melanoma. This permits the early diagnosis of the virus and the quick isolation and therapy necessary to stop the transmission of infection further. Deep learning (DL) and the standard non-parametric machine learning method are exemplified in the deep layer topologies of the convolutional neural network (CNN), which are neural network algorithms. The effectiveness of a CNN classifier was evaluated using data retrieved from the website https://dermnetnz.org/. The outcomes of the experiments show that the proposed method is superior in terms of diagnostic accuracy compared to the methodologies that are currently considered state of the art.

Pipelined Structure in the Classification of Skin Lesions Based on Alexnet CNN and SVM Model With Bi-Sectional Texture Features

“…The scheme is based on vectors and pixel classification of the images and it classifies the images into five categories of diseases: Psoriasis, Melanoma, Rosacea, Vitiligo, and Xanthelasma. The authors Reddy et al introduced a CNN model for skin disease classification [13] with the dataset taken from the website dermnetnz.org. This CNN method is a two-stage learning platform that provides an overall accuracy of 88.83% beats the other classification algorithms random forest (RF) and decision tree (DT) which have an accuracy in the 65%-75% range.…”

A Skin Disease Classification Model Based on DenseNet and ConvNeXt Fusion

“…It achieved a 95.1% and 83.5% accuracy and sensitivity, respectively, on the HAM10000 dataset. Allugunti et al [23] created a multi-class CNN model for diagnosing skin cancer. The proposed model makes a distinction between lesion maligna, superficial spreading, and nodular melanoma.…”

SkinLesNet: Classification of Skin Lesions and Detection of Melanoma Cancer Using a Novel Multi-Layer Deep Convolutional Neural Network

“…The models were trained and validated on the HAM10000 dataset, and the highest-performing generated an accuracy score of 91.2%. In addition to existing methods, such as border extraction utilizing XOR with regression logic, another CNN model was suggested in [ 43 ]. The datasets from PH2 and ISBI 2017 were utilized to train the model, which achieved a 97.8% accuracy rate.…”