Hybrid Color Texture Features Classification Through ANN for Melanoma

Abstract

Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of several objects like hair, reflections, air bubbles, and oils on almost all images. Active contour is one of the suitable methods with some drawbacks for the segmentation of irregular shapes. An entropy and morphology-based automated mask selection is proposed for the active contour method. The proposed method can improve the overall segmentation along with the boundary of melanoma images. In this study, features have been extracted to perform the classification on different texture scales like Gray level co-occurrence matrix (GLCM) and Local binary pattern (LBP). When four different moments pull out in six different color spaces like HSV, Lin RGB, YIQ, YCbCr, XYZ, and CIE L*a*b then global information from different colors channels have been combined. Therefore, hybrid fused texture features; such as local, color feature as global, shape features, and Artificial neural network (ANN) as classifiers have been proposed for the categorization of the malignant and non-malignant. Experimentations had been carried out on datasets Dermis, DermQuest, and PH2. The results of our advanced method showed superiority and contrast with the existing state-of-the-art techniques.

SkinLiTE: Lightweight Supervised Contrastive Learning Model for Enhanced Skin Lesion Detection and Disease Typification in Dermoscopic Images

“…Multiple studies have highlighted the pivotal role of machine learning and image processing in advancing the precision and efficiency of diagnosing skin diseases, signaling a shift towards the integration of AI in the field of dermatology [19][20][21][22][23][24][25]. For example, AlDera and Ben Othman [19] innovated a diagnostic model for conditions like acne and melanoma, implementing a comprehensive process encompassing image acquisition, preprocessing, segmentation, feature extraction, and classification, and achieved impressive accuracy with algorithms such as SVM, RF, and KNN.…”

“…Saghir and Hasan [21] concentrated on skin cancer detection, utilizing the differential analyzer algorithm and attaining a notable 96% classification accuracy. Mustafa, Jaffar, Iqbal, Abubakar, Alshahrani and Alghamdi [22] formulated a melanoma classification method using hybrid color texture features and neural network, outperforming existing methods across various datasets. Huong, Khang, Quynh, Thang, Canh and Sang [23] proposed a sophisticated deep and machine learning hybrid model for classifying monkeypox, obtaining high accuracy and F1-score.…”

The Human Eye Pupil Detection System Using BAT Optimized Deep Learning Architecture

“…Using Convolutional Neural Networks-based models, it will be possible to determine the pupil's centre [1] accurately. Artificial neural networks [2] are also improved the accuracy of medical diagnostic applications. Intelligent learning, enhanced network designs, and intelligent training methods are used, and CNN is thought to perform best in all vision-related applications.…”