Accurate diagnosis of liver diseases through the application of deep convolutional neural network on biopsy images

IF 1.1 Q4 BIOPHYSICS AIMS Biophysics Pub Date : 2023-01-01 DOI:10.3934/biophy.2023026
Soumyajit Podder, Abhishek Mallick, Sudipta Das, Kartik Sau, Arijit Roy
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Abstract

Accurate detection of non-alcoholic fatty liver disease (NAFLD) through biopsies is challenging. Manual detection of the disease is not only prone to human error but is also time-consuming. Using artificial intelligence and deep learning, we have successfully demonstrated the issues of the manual detection of liver diseases with a high degree of precision. This article uses various neural network-based techniques to assess non-alcoholic fatty liver disease. In this investigation, more than five thousand biopsy images were employed alongside the latest versions of the algorithms. To detect prominent characteristics in the liver from a collection of Biopsy pictures, we employed the YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8, and SSD models. A highlighting point of this paper is comparing the state-of-the-art Instance Segmentation models, including Mask R-CNN, U-Net, YOLOv5 Instance Segmentation, YOLOv7 Instance Segmentation, and YOLOv8 Instance Segmentation. The extent of severity of NAFLD and non-alcoholic steatohepatitis was examined for liver cell ballooning, steatosis, lobular, and periportal inflammation, and fibrosis. Metrics used to evaluate the algorithms' effectiveness include accuracy, precision, specificity, and recall. Improved metrics are achieved by optimizing the hyperparameters of the associated models. Additionally, the liver is scored in order to analyse the information gleaned from biopsy images. Statistical analyses are performed to establish the statistical relevance in evaluating the score for different zones.

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应用深度卷积神经网络对肝脏活检图像进行准确诊断
& lt; abstract>通过活检准确检测非酒精性脂肪性肝病(NAFLD)具有挑战性。人工检测疾病不仅容易出现人为错误,而且耗时。利用人工智能和深度学习,我们成功地展示了人工检测肝脏疾病的问题,并且精度很高。本文使用各种基于神经网络的技术来评估非酒精性脂肪性肝病。在这项调查中,超过5000张活检图像与最新版本的算法一起使用。为了从收集的活检图像中检测肝脏的突出特征,我们使用了YOLOv3, Faster R-CNN, YOLOv4, YOLOv5, YOLOv6, YOLOv7, YOLOv8和SSD型号。本文的一个重点是比较了最先进的实例分割模型,包括Mask R-CNN、U-Net、YOLOv5实例分割、YOLOv7实例分割和YOLOv8实例分割。检查NAFLD和非酒精性脂肪性肝炎的严重程度,检查肝细胞球囊化、脂肪变性、小叶和门静脉周围炎症以及纤维化。用于评估算法有效性的指标包括准确性、精密度、特异性和召回率。改进的度量是通过优化相关模型的超参数来实现的。此外,为了分析从活检图像中收集的信息,对肝脏进行评分。进行统计分析,以建立不同区域评分的统计相关性。& lt; / abstract>
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来源期刊
AIMS Biophysics
AIMS Biophysics BIOPHYSICS-
CiteScore
2.40
自引率
20.00%
发文量
16
审稿时长
8 weeks
期刊介绍: AIMS Biophysics is an international Open Access journal devoted to publishing peer-reviewed, high quality, original papers in the field of biophysics. We publish the following article types: original research articles, reviews, editorials, letters, and conference reports. AIMS Biophysics welcomes, but not limited to, the papers from the following topics: · Structural biology · Biophysical technology · Bioenergetics · Membrane biophysics · Cellular Biophysics · Electrophysiology · Neuro-Biophysics · Biomechanics · Systems biology
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