A Study and Analysis on Diagnosis of Melanoma Cancer With Deep Learning: A Case Study

A Study and Analysis on Diagnosis of Melanoma Cancer With Deep Learning: A Case Study

P. Yashashwini Reddy (Stanley College of Engineering and Technology for Women, India), C. Kishor Kumar Reddy (Stanley College of Engineering and Technology for Women, India), and Natassia Thandiwe Sithole (University of Johannesburg, South Africa)
Copyright: © 2024 |Pages: 16
DOI: 10.4018/979-8-3693-1082-3.ch011
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Abstract

The riskiest type of skin cancer is known as melanoma cancer, with more than millions of human populations identifying with this type in the last two decades around the world. Swift spreading nature to other areas of the body makes this type of cancer the most hazardous cancer among all other skin cancers. It can be reversed if identified at its primary stage, else chances of survival would be less if it is identified in its severe stage. There are several conventional methods to identify melanoma at primary stage performed by skin doctors, but there are a few limitations. To overcome the setbacks of conventional methods, artificial intelligence has been introduced to detect melanoma cancer. The application of concepts of artificial intelligence (AI) made a good enhancement in the field of medicine. A deep learning algorithm termed CNN is highly opted in melanoma detection as it shows appropriate outcomes.
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1. Introduction

The disease is termed as the condition in which the cells and organs of a living organism get damaged and perform abnormally. There are several types of diseases that affect living organisms, and it can be recognized by the symptoms and variations in the functioning that occur in the body. Some diseases will affect a particular organ and some diseases will affect the working of the whole body. Some diseases will sustain for a limited period, and some will sustain for a long period [1].

Diseases are classified as pandemic and epidemic. The diseases that only expand across several regions swiftly are known as epidemic diseases. Some instances of epidemic diseases are rubeola, poliomyelitis, etc. When diseases expand all around the globe, it leads to a pandemic disease. Some instances of pandemic diseases are COVID-19, tuberculosis, etc. Several types of diseases exist and table 1 portrays the most familiar diseases and how they are categorized.

The diseases are categorized based on factors like their duration of existence, organs it affects, the way it transmits, whether the disease is curable or not, root cause of the disease, on the basis of characteristics of the disease. This categorization will guide medical experts and researchers to have conclusions about the problem and it enhances the prediction and treatment of any kind of issues related to our health.

Table 1.
Stratification of most familiar diseases
S.NoType of the diseaseInstances
1Communicable diseaseHIV, COVID-19, Chickenpox
2Enduring diseaseCancer, Heart disease, Alzheimer
3Genetic disorderHemophilia, Autism
4Psychological disorderPost traumatic stress disorder (PTSD), Insomnia
5Due to improper dietOverweight, Type 2 diabetes
6Due to ecological issuesAsthma, Cancers of few types
7Autoimmune disorderPsoriasis, Dermatomyositis
8Diseases of digestive systemGluten-sensitive enteropathy, Cholelithiasis

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