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Parametric Examination on Optimized Deep Learning based Melanoma Detection by Abhinandan Kumar Tiwari; Manoj Kumar Mishra; Amiya Ranjan Panda; Bikramaditya Panda is a scholarly article available to read on EtoBox.

What is Parametric Examination on Optimized Deep Learning based Melanoma Detection about?

The occurrence of tumors in the skin grows gradually. In the early stage, examination by an experienced dermatologist will reliably identify malignant skin tumors; however, extensive screening by dermatologists is not feasible for each single patient. Many studies have been undergone to improve dermatologist-level, computer-aided diagnostics are underway to overcome this challenge. After the advent of deep-learning technology, the detection mechanism has become significant in the medical field. This paper intends to suggest a new paradigm for the diagnosis of melanoma, which comprises of four main stages. (i) preprocessing (ii) segmentation (iii) extraction of features and (iv) classification. Pre-processing is mainly carried out, where the input image is susceptible to smoothening and resizing of the edges. Segmentation is subsequently performed through the Otsu thresholding mechanism. The GLCM features along with the Higher Order Uniform Moment Induced-Local Binary Patterns (HOSMI-LBP) features are extracted. The combination of Convolutional Neural Network (CNN) and Neural Network (NN) architectures is used to define the classification of melanoma as either benign or malignant. T

Author

Abhinandan Kumar Tiwari; Manoj Kumar Mishra; Amiya Ranjan Panda; Bikramaditya Panda

Publisher

IEEE

Published

2021

Language

EN