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Computer-Assisted Analysis of Histopathological Images: A Comprehensive Review
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Author(s): Pranshu Saxena (School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India), Sanjay Kumar Singh (Guru Gobind Singh Indraprastha University, India), Gaurav Srivastav (Krishna Institute of Engineering and Technology, Ghaziabad, India)and Rashid Mamoon (School of Information Communication and Technology, Bahrain Polytechnic, Bahrain)
Copyright: 2025
Pages: 44
Source title:
Computer-Assisted Analysis for Digital Medicinal Imagery
Source Author(s)/Editor(s): Amit Sinha (ABES Engineering College, Ghaziabad, India), Pranshu Saxena (School of Computer Science Engineering and Technology, Bennett University, Greater Noida, India), Sanjay Kumar Singh (University School of Automation and Robotics, Guru Gobind Singh Indraprastha University, East Delhi, India)and Harikesh Singh (JSS Academy of Technical Education, Noida, India)
DOI: 10.4018/979-8-3693-5226-7.ch004
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Abstract
Histopathological image analysis is a specialised area of medical imaging that involves the careful examination and interpretation of tissue samples to diagnose and investigate diseases. We begin by reviewing the traditional methods of histopathological image analysis and their limitations, setting the stage for the necessity of computational approaches. The study then delves into the core components of computer-assisted analysis, including preprocessing techniques, feature extraction, and classification algorithms. Preprocessing steps such as staining normalization, noise reduction, and image segmentation are critical for preparing raw images for further analysis. Feature extraction methods, ranging from handcrafted features to DL based features, are discussed in detail, emphasizing their role in capturing relevant tissue characteristics. The classification stage employs various machine learning models, including SVM, RF, and NN, with a focus on CNN due to their superior performance in image recognition tasks.
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