Skin lesion classification in dermoscopic images using stacked Convolutional Neural Network

2021 ◽  
Author(s):  
Ahmad Hameed ◽  
Muhammad Umer ◽  
Umair Hafeez ◽  
Hassan Mustafa ◽  
Ahmed Sohaib ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Classification

Depthwise Separable Convolutional Neural Network for Skin Lesion Classification

2019 ◽  
Author(s):  
Sara Hosseinzadeh Kassani ◽  
Peyman Hosseinzadeh Kassani ◽  
Michal J. Wesolowski ◽  
Kevin A. Schneider ◽  
Ralph Deters
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Lesion Classification

scholarly journals Deep Semantic Segmentation and Multi-Class Skin Lesion Classification Based on Convolutional Neural Network

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 129668-129678
Author(s):  
Muhammad Almas Anjum ◽  
Javaria Amin ◽  
Muhammad Sharif ◽  
Habib Ullah Khan ◽  
Muhammad Sheraz Arshad Malik ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Semantic Segmentation ◽  
Lesion Classification

scholarly journals Optimized Convolutional Neural Network Models for Skin Lesion Classification

2022 ◽  
Vol 70 (2) ◽  
pp. 2131-2148
Author(s):  
Juan Pablo Villa-Pulgarin ◽  
Anderson Alberto Ruales-Torres ◽  
Daniel Arias-Garz髇 ◽  
Mario Alejandro Bravo-Ortiz ◽  
Harold Brayan Arteaga-Arteaga ◽  
...  
Keyword(s):  
Neural Network ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Network Models ◽  
Neural Network Models ◽  
Lesion Classification

scholarly journals Skin Lesion Classification With Deep Convolutional Neural Network: Process Development and Validation

10.2196/18438 ◽  
2020 ◽  
Vol 3 (1) ◽  
pp. e18438
Author(s):  
Arnab Ray ◽  
Aman Gupta ◽  
Amutha Al
Keyword(s):  
Neural Network ◽  
Skin Cancer ◽  
Skin Lesion ◽  
Convolutional Neural Network ◽  
Process Development ◽  
Early Stage ◽  
Deep Convolutional Neural Network ◽  
Fine Tuning ◽  
Network Process ◽  
Lesion Classification

Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are highly suitable for classifying skin cancer images.

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