A Comparative Analysis on Deep Learning Techniques for Skin Cancer Detection and Skin Lesion Segmentation

2021 ◽  
Author(s):  
Saurav Vinod ◽  
Manoj V. Thomas
Keyword(s):  
Deep Learning ◽  
Comparative Analysis ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Cancer Detection ◽  
Lesion Segmentation ◽  
Learning Techniques ◽  
Skin Cancer Detection

scholarly journals Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures

2021 ◽  
Vol 2 (01) ◽  
pp. 41-51
Author(s):  
Jwan Saeed ◽  
Subhi Zeebaree
Keyword(s):  
Neural Networks ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Deep Convolutional Neural Networks ◽  
Color Structure ◽  
Malignant Skin ◽  
Dermatological Disorders ◽  
Detection And Diagnosis ◽  
Skin Cancer Detection

Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.

Skin Cancer Detection and Severity Prediction Using Computer Vision and Deep Learning

2021 ◽  
pp. 295-304
Author(s):  
Sangeeta Parshionikar ◽  
Renjit Koshy ◽  
Aman Sheikh ◽  
Gauravi Phansalkar
Keyword(s):  
Computer Vision ◽  
Deep Learning ◽  
Skin Cancer ◽  
Cancer Detection ◽  
Severity Prediction ◽  
Skin Cancer Detection

scholarly journals A Probabilistic-Based Deep Learning Model for Skin Lesion Segmentation

2021 ◽  
Vol 11 (7) ◽  
pp. 3025
Author(s):  
Adekanmi Adeyinka Adegun ◽  
Serestina Viriri ◽  
Muhammad Haroon Yousaf
Keyword(s):  
Deep Learning ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Probabilistic Model ◽  
Learning System ◽  
Gaussian Kernel ◽  
Complex Nature ◽  
Low Contrast ◽  
Learning Techniques ◽  
Cancer Diseases

The analysis and detection of skin cancer diseases from skin lesion have always been tedious when done manually. The complex nature of skin lesion images is one of the key reasons for this. The skin lesion images contain noise and artifacts such as hairs, oil and bubbles, blood vessels, and skin lines. They also have variegated colors, low contrast, and irregular borders. Various computational approaches have been designed in the past for aiding in the detection and diagnosis of skin cancer diseases using skin lesion images. The existing techniques have been limited due to the interference of the aforementioned features of skin lesion. Recently, machine learning techniques, in particular the deep learning techniques have been used for the detection of skin cancer. However, they are still limited to the fuzzy and irregular borders of skin lesion images coupled with the low contrast that exists between the diseased lesion and healthy tissues. In this paper, we utilized a probabilistic model for the enhancement of a fully convolutional network-based deep learning system to analyze and segment skin lesion images. The probabilistic model employs an efficient mean-field approximate probabilistic inference approach with a fully connected conditional random field that utilizes a Gaussian kernel. The probabilistic model further performs a refinement of skin lesion borders. The whole framework is tested and evaluated on publicly available skin lesion image datasets of ISBI 2017 and PH2. The system achieved a better performance, having an accuracy of 98%.

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