Abstract
Image classification is central to the big data revolution in medicine. Improved information processing methods for diagnosis and classification of digital medical images have shown to be successful via deep learning approaches. As this field is explored, there are limitations to the performance of traditional supervised classifiers. This paper outlines an approach that is different from the current medical image classification tasks that view the issue as multi-class classification. We performed a hierarchical classification using our Hierarchical Medical Image classification (HMIC) approach. HMIC uses stacks of deep learning models to give particular comprehension at each level of the clinical picture hierarchy. For testing our performance, we use biopsy of the small bowel images that contain three categories in the parent level (Celiac Disease, Environmental Enteropathy, and histologically normal controls). For the child level, Celiac Disease Severity is classified into 4 classes (I, IIIa, IIIb, and IIIC).
Highlights
Introduction and Related WorksAutomatic diagnosis of diseases based on medical image categorization has become increasingly challenging over the last several years [1,2,3]
Information 2020, 11, 318 just label medical images-based specialized areas, but to organize them within an overall field with the accompanying sub-field which we have done in this paper via Hierarchical Medical Image Classification (HMIC)
The Celiac Disease (CD) evaluation measure for the parent level is as follows: precision is equal to 91.12 ± 0.3208, recall is equal to 88.71 ± 0.3569, and F1-score is equal to 89.90 ± 1.2778
Summary
Introduction and Related WorksAutomatic diagnosis of diseases based on medical image categorization has become increasingly challenging over the last several years [1,2,3]. Areas of research involving deep learning architectures for image analysis have grown in the past few years with an increasing interest in their exploration and understanding of the domain application [3,4,5,6,7]. Deep learning models achieved state-of-the-art results in a wide variety of fundamental tasks such as image classification in the medical domain [8,9]. This growth has raised questions regarding classification of sub-types of disease across a range of disciplines including Cancer (e.g., stage of cancer), Celiac Disease (e.g., Marsh Score Severity Class), and Chronic Kidney Disease (e.g., Stage 1–5) among others [10]. Hierarchical models combat the problem of unbalanced medical image datasets for training the model and have been successful for other domains [11,12]
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