A Review on Multi-organ Cancer Detection Using Advanced Machine Learning Techniques

Tariq       Sadad; Amjad       Rehman; Ayyaz       Hussain; Aaqif    Afzaal    Abbasi; Muhammad    Qasim    Khan
doi:10.2174/1573405616666201217112521
Abstract

Abnormal behaviors of tumors pose a risk to human survival. Thus, the detection of cancers at their initial stage is beneficial for patients and lowers the mortality rate. However, this can be difficult due to various factors related to imaging modalities, such as complex background, low contrast, brightness issues, poorly defined borders and the shape of the affected area. Recently, computer-aided diagnosis (CAD) models have been used to accurately diagnose tumors in different parts of the human body, especially breast, brain, lung, liver, skin and colon cancers. These cancers are diagnosed using various modalities, including computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology. The aim of this review was to investigate existing approaches for the diagnosis of breast, brain, lung, liver, skin and colon tumors. The review focuses on decision-making systems, including handcrafted features and deep learning architectures for tumor detection.
Keywords: Classification, colonoscopy, mammography, healthcare, public health, CT, MRI.
Graphical Abstract

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DOI https://dx.doi.org/10.2174/1573405616666201217112521	Print ISSN 1573-4056
Publisher Name Bentham Science Publisher	Online ISSN 1875-6603
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