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A Review on Multi-Organs Cancer Detection using Advanced Machine Learning Techniques


Tariq Sadad, Amjad Rehman, Ayyaz Hussain, Aaqif Afzaal Abbasi* and Muhammad Qasim Khan   Pages 1 - 11 ( 11 )


Abnormality behavior of the tumor is risky for human survival. Thus, finding cancer at the initial stage is beneficial for the reduction of mortality rate. Although it is not easy due to various factors concern with modalities, such as complex background, poor contrast, brightness issues, ill-defined borders, and shape of the infected area. Recently computer-aided systems (CAD) accomplish accurate diagnoses using different parts of the human body especially tumors detection in breast, brain, lung, liver, skin and colon cancer. These human organs are evaluated using several diagnostic procedures, for instance, computed tomography (CT), magnetic resonance imaging (MRI), colonoscopy, mammography, dermoscopy and histopathology etc. The main intention of this research work is to investigate existing approaches for breast, brain, lung, liver, skin and finding of colon tumor. The study is conducted in terms of decision-making systems including handcrafted features and deep learning architectures employed for tumor detection.


Classification, colonoscopy, CT, mammography, MRI, Abnormality behavior, dermoscopy


Department of Computer Science, University of Central Punjab, Artificial Intelligence & Data Analytics Lab Prince Sultan University Riyadh 11586, Department of Computer Science, Quaid-i-Azam University, Islamabad, Department of Software Engineering, Foundation University, Islamabad, Department of Computer Science, COMSATS University (Attock Campus) Islamabad

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