Deep Learning Methods for Lung Cancer Nodule Classification: A Survey

Illa, Pavan Kumar; Kumar, T. Senthil; Hussainy, F. Syed Anwar

doi:10.13052/jmm1550-4646.18213

Cited by 1 publication

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on image classification, medical image classification has been investigated to improve classification performance and to provide medical services remotely. [11] surveys deep learning-based methods for lung nodule classification and reports that [12] and [13] show the best performance on lung nodule classification with [12] achieving 88.96% and [13] 89.99% accuracy.…”

Section: Medical Image Classificationmentioning

confidence: 99%

Deep and Lightweight Neural Network for Histopathological Image Classification

Kim

Yoon

2022

JMM

View full text Add to dashboard Cite

Breast cancer is a fatal disease affecting women, and early detection and proper treatment are crucial. Classifying medical images correctly is the first and most important step in the cancer diagnosis stage. Deep learning-based classification methods in various domains demonstrate advances in accuracy. However, as deep learning improves, the layers of neural networks get deeper, raising challenges, such as overfitting and gradient vanishing. For instance, a medical image is simpler than an ordinary one, making it vulnerable to overfitting issues. We present breast histopathological classification methods with two deep neural networks, Xception and LightXception with aid of voting schemes over split images. Most deep neural networks classify thousands classes of images, but the breast histopathological image classes are far fewer than those of other image classification tasks. Because the BreakHis dataset is relatively simpler than typical image datasets, such as ImageNet, applying the conventional highly deep neural networks may suffer from the aforementioned overfitting or gradient vanishing problems. Additionally, highly deep neural networks require more resources, leading to high computational costs. Consequently, we propose a new network; LightXception by cutting off layers at the bottom of the Xception network and reducing the number of channels of convolution filters. LightXception has only about 35% of parameters compared to those of the original Xception network with minimal expense on performance. Based on images with 100X magnification factor, the performance comparisons for Xception vs. LightXception are 97.42% vs. 97.31% on classification accuracy, 97.42% vs. 97.42% on recall, and 99.26% vs. 98.67% of precision.

show abstract