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.
