Tariq Sadad, Amjad Rehman, Ayyaz Hussain, Aaqif Afzaal Abbasi* and Muhammad Qasim Khan Pages 686 - 694 ( 9 )
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.
Classification, colonoscopy, mammography, healthcare, public health, CT, MRI.
Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Artificial Intelligence & Data Analytics Lab CCIS 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