Generative Adversarial Network Image Synthesis Method for Skin Lesion Generation and Classification
سال انتشار: 1400
نوع سند: مقاله ژورنالی
زبان: انگلیسی
مشاهده: 174
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شناسه ملی سند علمی:
JR_JMSI-11-4_003
تاریخ نمایه سازی: 28 تیر 1402
چکیده مقاله:
Background: One of the common limitations in the treatment of cancer is in the early detection of
this disease. The customary medical practice of cancer examination is a visual examination by the
dermatologist followed by an invasive biopsy. Nonetheless, this symptomatic approach is timeconsuming
and prone to human errors. An automated machine learning model is essential to capacitate
fast diagnoses and early treatment. Objective: The key objective of this study is to establish a fully
automatic model that helps Dermatologists in skin cancer handling process in a way that could improve
skin lesion classification accuracy. Method: The work is conducted following an implementation of a
Deep Convolutional Generative Adversarial Network (DCGAN) using the Python-based deep learning
library Keras. We incorporated effective image filtering and enhancement algorithms such as bilateral
filter to enhance feature detection and extraction during training. The Deep Convolutional Generative
Adversarial Network (DCGAN) needed slightly more fine-tuning to ripe a better return. Hyperparameter
optimization was utilized for selecting the best-performed hyperparameter combinations and several
network hyperparameters. In this work, we decreased the learning rate from the default ۰.۰۰۱ to ۰.۰۰۰۲,
and the momentum for Adam optimization algorithm from ۰.۹ to ۰.۵, in trying to reduce the instability
issues related to GAN models and at each iteration the weights of the discriminative and generative
network were updated to balance the loss between them. We endeavour to address a binary classification
which predicts two classes present in our dataset, namely benign and malignant. More so, some wellknown
metrics such as the receiver operating characteristic -area under the curve and confusion matrix
were incorporated for evaluating the results and classification accuracy. Results: The model generated
very conceivable lesions during the early stages of the experiment and we could easily visualise a
smooth transition in resolution along the way. Thus, we have achieved an overall test accuracy of ۹۳.۵%
after fine-tuning most parameters of our network. Conclusion: This classification model provides spatial
intelligence that could be useful in the future for cancer risk prediction. Unfortunately, it is difficult to
generate high quality images that are much like the synthetic real samples and to compare different
classification methods given the fact that some methods use non-public datasets for training.
کلیدواژه ها:
نویسندگان
Freedom Mutepfe
Department of Computer Science and Engineering, School of Science and Engineering, Khazar University, Baku, Azerbaijan
Behnam Kiani Kalejahi
Department of Computer Science and Engineering, School of Science and Engineering, Khazar University, Baku, Azerbaijan- Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Saeed Meshgini
Department of Biomedical Engineering, Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz, Iran
Sebelan Danishvar
Department of Electronic and Computer Engineering, Brunel University, London, UK