Disease Prediction using Machine Learning, Deep Learning and Data Analytics

Computer-aided Bio-medical Tools for Disease Identification

Author(s): E. Francy Irudaya Rani, T. Lurthu Pushparaj and E. Fantin Irudaya Raj * .

Pp: 52-79 (28)

DOI: 10.2174/9789815179125124010009

* (Excluding Mailing and Handling)


The health expert’s crucial task is to interpret the output and treat the disease accordingly. They may delay the decision-making during emergencies. To address this issue, research on smart tools for biomedical applications is much needed which may help in making accurate decisions at the earliest stage. Discovery in medicinal research requires state-of-the-art computer-based tools for diagnosing and treating complex diseases such as cancer, COVID-19, SARS-Cov, MERS-Cov, tuberculosis, brain disorders, heart, and lung-related chronic infections. Among various diagnostic methods, image-based disease identification stands out as the most prominent approach for detecting new and complex diseases. A well-trained computerized biomedical system can provide physicians with enhanced support for early disease detection. Biomedical images are typically acquired from various sources, including CT, ultrasound, MRI, dermoscopy, X-ray, biopsy, and endoscopy. Presently, a wide range of image-analysis procedures are available for biomedical images. These procedures involve image acquisition, pre-processing, segmentation, feature extraction, and classification, all contributing to improved disease decision accuracy. Although many biomedical images are available online free of cost, the proper procedure must be followed to select appropriate images from databases and enhance their quality. This is important for effectively training image-processing algorithms and increasing their efficiency. This leads to improved instrument performance and more valuable insights into the diseases under study. It also handles complex and vast image data to detect early signs of unusual signals, growth, inflammation, cell damage, protein sequence changes, and blockages. Additionally, it should be user-friendly and convincing to health experts to identify hidden biological issues. This chapter emphasizes the power of computerized tools in image analysis and disease detection. It also focuses on recent developments in the field of medicinal research.

Keywords: Biomedical image processing, CAD, Combined algorithm, Cancer diagnosis, Covid-19, Classification, Feature extraction, Pre-processing, Segmentation.

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