Deep Learning Approach for Medical Image Analysis

Adekanmi Adeyinka Adegun,Roseline Oluwaseun Ogundokun,Serestina Viriri,Mario Versaci

doi:10.1155/2021/6215281

Adekanmi Adeyinka Adegun, Roseline Oluwaseun Ogundokun + Show 2 more

Open Access

https://doi.org/10.1155/2021/6215281

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Abstract

Localization of region of interest (ROI) is paramount to the analysis of medical images to assist in the identification and detection of diseases. In this research, we explore the application of a deep learning approach in the analysis of some medical images. Traditional methods have been restricted due to the coarse and granulated appearance of most of these images. Recently, deep learning techniques have produced promising results in the segmentation of medical images for the diagnosis of diseases. This research experiments on medical images using a robust deep learning architecture based on the Fully Convolutional Network- (FCN-) UNET method for the segmentation of three samples of medical images such as skin lesion, retinal images, and brain Magnetic Resonance Imaging (MRI) images. The proposed method can efficiently identify the ROI on these images to assist in the diagnosis of diseases such as skin cancer, eye defects and diabetes, and brain tumor. This system was evaluated on publicly available databases such as the International Symposium on Biomedical Imaging (ISBI) skin lesion images, retina images, and brain tumor datasets with over 90% accuracy and dice coefficient.

Highlights

Segmentation is the key process of identification of region of interest (ROI) of a disease region to assist in the diagnosis of diseases
Diseases such as brain tumors, diabetes retinopathy, skin cancer, and liver tumor have been successfully diagnosed through the analysis of Magnetic Resonance Imaging (MRI) scans, retina vessel images, skin lesion images, and liver tumor scan, respectively, through the use of deep learning techniques [1]
A pixel-wise deep learning approach that employed variants of Fully Convolutional Networks (FCNs) such as Fully Convolutional Network- (FCN-)AlexNet, FCN-32, FCN-16s, and FCN-8s has been used for semantic segmentation of breast lesions [7]. e system used pretrained ImageNet-based models and transition of training to address information deficiency issues and was competent to classify two categories of benign and malignant

Summary

Introduction

Segmentation is the key process of identification of ROI of a disease region to assist in the diagnosis of diseases It is very important in medical imaging where localization is paramount to the analysis of scans. Recent advancement in machine learning methodologies has led to the development of deep learning techniques in the field of medical images analysis for the diagnosis of various diseases [1]. Diseases such as brain tumors, diabetes retinopathy, skin cancer, and liver tumor have been successfully diagnosed through the analysis of MRI scans, retina vessel images, skin lesion images, and liver tumor scan, respectively, through the use of deep learning techniques [1]. E proposed system efficiently performs the analysis of retina images and identifies the optic disc ROI. Performance evaluating metrics such as dice coefficient, accuracy, etc., have been utilized to evaluate the model

Review of Related Works

Experiment and Analysis

Evaluation Metrics

Comparison of the System Performance with the Existing Systems

Method