Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection

Authors

  • Hemlata Sahu Amity School of Engineering, Amity University, Raipur, India
  • Ramgopal Kashyap Amity School of Engineering, Amity University, Raipur, India
  • Surbhi Bhatia Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia
  • Bhupesh Kumar Dewangan Department of Computer Science and Engineering, OP Jindal University, Raigarh, India
  • Nora A. Alkhaldi Department of Information Systems, College of Computer Science and Information Technology, King Faisal University, Saudi Arabia
  • Samson Anosh Babu Krishna Chaitanya Institute of Technology and Sciences, Markapur, Andhra Pradesh, India
  • Senthilkumar Mohan School of Information Technology and Engineering, Vellore Institute of Technology, Vellore 632014, India

DOI:

https://doi.org/10.37256/cm.4420232496

Keywords:

deep learning, deep features, medical image classification, DL techniques, healthcare sector analysis

Abstract

In this paper, artificial intelligence (AI) and the ideas of machine learning (ML) and deep learning (DL) are introduced gradually. Applying ML techniques like deep neural network (DNN) models has grown in popularity in recent years due to the complexity of healthcare data, which has been increasing. To extract hidden patterns and some other crucial information from the enormous amount of health data, which traditional analytics are unable to locate in a fair amount of time, ML approaches offer cost-effective and productive models for data analysis. We are encouraged to pursue this work because of the quick advancements made in DL approaches. The idea of DL is developing from its theoretical foundations to its applications. Modern ML models that are widely utilized in academia and industry, mostly in image classification and natural language processing, including DNN. Medical imaging technologies, medical healthcare data processing, medical disease diagnostics, and general healthcare all stand to greatly benefit from these developments. We have two goals: first, to conduct a survey on DL techniques for medical pictures, and second, to develop DL-based approaches for image classification. This paper is mainly targeted towards understanding the feasibility and different processes that could be adopted for medical image classification; for this, we perform a systematic literature review. A review of various existing techniques in terms of medical image classification indicates some shortcomings that have an impact on the performance of the whole model. This study aims to explore the existing DL approaches, challenges, brief comparisons, and applicability of different medical image processing are also studied and presented. The adoption of fewer datasets, poor use of temporal information, and reduced classification accuracy all contribute to the lower performance model, which is addressed. The study provides a clear explanation of contemporary developments, cutting-edge learning tools, and platforms for DL techniques.

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Published

2023-11-17

How to Cite

1.
Sahu H, Kashyap R, Bhatia S, Dewangan BK, Alkhaldi NA, Anosh Babu S, Mohan S. Analysis of Deep Learning Methods for Healthcare Sector - Medical Imaging Disease Detection. Contemp. Math. [Internet]. 2023 Nov. 17 [cited 2024 May 28];4(4):830-52. Available from: https://ojs.wiserpub.com/index.php/CM/article/view/2496