Abstract
Medical imaging has become an important part of diagnosing, early detection, and treating cancers. In this paper, a comprehensive survey on various image processing techniques for medical images specifically examined cancer diseases for most body sections. These sections are Bone, Liver, Kidney, Breast, Lung, and Brain. Detection of medical imaging involves different stages such as classification, segmentation, image pre-processing, and feature extraction. With regard to this work, many image processing methods will be studied, over 10 surveys reviewing classification, feature extraction, and segmentation methods utilized image processing for cancer diseases for most body's sections are clearly reviewed.
Highlights
The human body is made up of several types of cells
Medical imaging can be defined as the method and process to create visual representation regarding the body's interior for medical intervention and clinical analysis, the visual representations related to the functions of a few tissues or organs [1]
Efficient medical images might be of high importance to aid in treatment and diagnosis; they might be significant in the domain of education for health-care students through explaining with such images help them in their study [2]
Summary
The human body is made up of several types of cells. There are various tumor types with different characteristics that are often observed in the human body. Ambalkar et al [12] This study suggested a tumor detection method with the use of machine learning, while the MRI images are the dataset for performance analysis. Hüseyin Kutlu et al [15] This study suggested a new brain and liver tumor classification approach with the use of CNN in feature extraction, DWT in signal processing, and LSTM in signal classification. Goran Jakimovski et al [17], In this study, scans of CT has been utilized for the training of a regular CDNN and a double convolutional Deep Neural Network (CDNN) Those topologies have been tested against the images of lung cancer for the determination of Tx cancer stage where those topologies have the ability of detecting the likelihood of lung cancer.
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