Convolutional Neural Network-Based Skin Lesion Classification With Variable Nonlinear Activation Functions

doi:10.1109/ACCESS.2022.3196911

2022-01-01

M. A. Rasel

Unaizah Obaidellah *

Sameem Abdul Kareem

DOI：10.1109/ACCESS.2022.3196911

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摘要

En 中文

One of the worst forms of skin cancer is melanoma which can be curable if it is diagnosed at an early stage. The earlier the cancer is diagnosed, the better is the outcome. The risk of death from melanoma is directly related to the delay in identifying a lesion. A Deep Learning-based computer diagnosing system can be an automated solution in clinical assessments to overcome this problem. Convolutional Neural Network (CNN) can help to improve the classification rate of skin lesions from dermoscopic images without the need for any human assistance. The linear and nonlinear activation functions act as a node placed at hidden layers or output layers of a Neural Network to play a role in deciding whether that node should pass information to the following layer or not. This critical mathematical mechanism influences the accuracy rate of the CNN. To obtain acceptable performance, CNN requires a large amount of training data. This research shows how fast and effective different types of nonlinear activation functions work on a CNN with limited image datasets. Experimental analysis reveals that the proposed CNN model with parameterized Leaky ReLU function outperforms (97.50% accuracy, 98.00% precision, and 98.00% sensitivity) the same network with distinct nonlinear activation functions for the problem of melanoma recognition by classifying skin lesion into three classes. All experimental studies are carried out using images from PH2 (a dermoscopic image database obtained at the Hospital Pedro Hispano Dermatology Service in Matosinhos, Portugal) and International Skin Imaging Collaboration (ISIC) archive datasets.

Keyword:

Lesions

Convolutional neural networks

Skin

Melanoma

Image segmentation

Deep learning

Training

Convolutional neural network

deep learning

image augmentation

linear activation function