HMIC: Hierarchical Medical Image Classification, A Deep Learning Approach

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).

A review on deep learning in medical image analysis

“…Goal: Recognize where organs or other organs are located in space (2 and 3D) or in time, landmarks or objects (video/4D) and general deep learning method used here is to identify the intersection of interest in using separate CNNs with each 2D plane running a 3D image [ 18 ]. Localization [ 19 ] for biological architectures would be a fundamental prerequisite for different medical image investigation initiatives.…”

“…A powerful and reliable method for medical image classification, modality classification was used in this study, which can be used to extract clinical data from vast medical repositories. The method was created by combining the transfer learning principle with a pre-trained ResNet50 model for optimized feature retrieval and also classification with TLRN-LDA (Linear Discriminant Analysis) [ 18 ]. In Image CLEF benchmark (31-class image dataset), the evolved technique gives 88 percent of average accuracy in classification that is up to 11 percent higher related to the current state-of-the-art methods with the same image datasets [ 19 ].…”

“…Transfer learning [ 17 ] is the viable component that can give a promising arrangement transferring information from nonexclusive article acknowledgment errands to area explicit undertakings. Here utilized a deep convolutional neural network, called DeTraC (Decompose, Transfer, Compose) with the characterization of Covid-19 +ve cases of chest X-ray data [ 18 ]. DeTraC can able to manage any kind of abnormalities with the picture dataset by examining its image class limits utilizing image class decay system [ 19 ].…”

“…Here [ 18 ] played out various levelled arrangements utilizing this HMIC (Hierarchical Medical Image characterization) method. This utilizes heaps of deep learning technique models for giving specific perception at every degree of medical image orders [ 19 , 20 ].…”

IoT enabled depthwise separable convolution neural network with deep support vector machine for COVID-19 diagnosis and classification

“…In all individual matrix, is convolved with its equivalent kernel matrix , and bias of At last, an activation function is implemented for all individuals elements. The biases and weights are modified for constituting able feature detection filters behind the back‐propagation (BP) phase in CNN training [ 16 ]. A feature map filters are implemented across every 3 channels.…”