Survey on machine and deep learning methods used in CT scan brain diseases diagnosis

A CT scan of the brain is the first option for diagnosing intracranial disease. However, as the number of patients increases, there is a shortage of doctors who can make a diagnosis. In recent years, many computer-aided diagnostic algorithms have been developed to assist physicians in diagnosing and...

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Bibliographic Details
Main Authors: Majeed, Mohammad A. Abdul, Alrawi, Azmi Tawfeq, Okashi, Omar Munthir Al
Format: Conference Proceeding
Language:English
Subjects:
Algorithms
Brain
Computed tomography
Deep learning
Diagnosis
Machine learning
Medical imaging
Medical personnel
Physicians
Online Access:Get full text
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Summary:A CT scan of the brain is the first option for diagnosing intracranial disease. However, as the number of patients increases, there is a shortage of doctors who can make a diagnosis. In recent years, many computer-aided diagnostic algorithms have been developed to assist physicians in diagnosing and saving time. Computed Tomography (CT) is the most widely used diagnostic imaging method for diagnosing brain disorders due to its speed, cost-effectiveness, and wide range of applications. However, because of the brain’s complex structure, finding bleeding points and lesions is difficult, and doctors have a difficult time diagnosing the disease quickly and accurately. This article gives an overview of recent machine learning and deep learning methods for detecting and classifying brain diseases from CT images.
ISSN:0094-243X
1551-7616
DOI:10.1063/5.0190368