Reduction of overfitting on the highly imbalanced ISIC-2019 skin dataset using deep learning frameworks

IF 1.7 3区 医学 Q3 INSTRUMENTS & INSTRUMENTATION
Erapaneni Gayatri, S.L. Aarthy
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Abstract

BACKGROUND:With the rapid growth of Deep Neural Networks (DNN) and Computer-Aided Diagnosis (CAD), more significant works have been analysed for cancer related diseases. Skin cancer is the most hazardous type of cancer that cannot be diagnosed in the early stages. OBJECTIVE:The diagnosis of skin cancer is becoming a challenge to dermatologists as an abnormal lesion looks like an ordinary nevus at the initial stages. Therefore, early identification of lesions (origin of skin cancer) is essential and helpful for treating skin cancer patients effectively. The enormous development of automated skin cancer diagnosis systems significantly supports dermatologists. METHODS:This paper performs a classification of skin cancer by utilising various deep-learning frameworks after resolving the class Imbalance problem in the ISIC-2019 dataset. A fine-tuned ResNet-50 model is used to evaluate the performance of original data, augmented data, and after by adding the focal loss. Focal loss is the best technique to solve overfitting problems by assigning weights to hard misclassified images. RESULTS:Finally, augmented data with focal loss is given a good classification performance with 98.85% accuracy, 95.52% precision, and 95.93% recall. Matthews Correlation coefficient (MCC) is the best metric to evaluate the quality of multi-class images. It has given outstanding performance by using augmented data and focal loss.
利用深度学习框架减少高度不平衡的 ISIC-2019 皮肤数据集上的过度拟合现象
背景:随着深度神经网络(DNN)和计算机辅助诊断(CAD)的快速发展,针对癌症相关疾病的分析工作也越来越重要。皮肤癌是最危险的癌症类型,无法在早期阶段诊断出来。目的:皮肤癌的诊断正成为皮肤科医生面临的一项挑战,因为异常病变在初期看起来就像普通的痣。因此,早期识别病变(皮肤癌的起源)对于有效治疗皮肤癌患者至关重要。皮肤癌自动诊断系统的巨大发展为皮肤科医生提供了重要支持。方法:本文在解决 ISIC-2019 数据集中的类不平衡(class Imbalance)问题后,利用各种深度学习框架对皮肤癌进行了分类。微调后的 ResNet-50 模型用于评估原始数据、增强数据以及添加焦点损失后数据的性能。焦点损失是解决过拟合问题的最佳技术,它为难以分类的错误图像分配权重。结果:最后,带有焦点损失的增强数据具有良好的分类性能,准确率为 98.85%,精确率为 95.52%,召回率为 95.93%。马修斯相关系数(MCC)是评估多类图像质量的最佳指标。通过使用增强数据和焦点损失,该指标表现出色。
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来源期刊
CiteScore
4.90
自引率
23.30%
发文量
150
审稿时长
3 months
期刊介绍: Research areas within the scope of the journal include: Interaction of x-rays with matter: x-ray phenomena, biological effects of radiation, radiation safety and optical constants X-ray sources: x-rays from synchrotrons, x-ray lasers, plasmas, and other sources, conventional or unconventional Optical elements: grazing incidence optics, multilayer mirrors, zone plates, gratings, other diffraction optics Optical instruments: interferometers, spectrometers, microscopes, telescopes, microprobes
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