Deep Learning for Medical Image Processing: Overview, Challenges and Future

By Muhammad Imran Razzak, Saeeda Naz, Ahmad Zaib. arXiv.org, 2017

1,183 citations recorded by the source corpus.

The health care sector is totally different from any other industry. It is a high priority sector and consumers expect the highest level of care and services regardless of cost. The health care sector has not achieved society’s expectations, even though the sector consumes a huge percentage of national budgets. Mostly, the interpretations of medical data are analyzed by medical experts. In terms of a medical expert interpreting images, this is quite limited due to its subjectivity and the complexity of the images; extensive variations exist between experts and fatigue sets in due to their heavy workload. Following the success of deep learning in other real-world applications, it is seen as also providing exciting and accurate solutions for medical imaging, and is seen as a key method for future applications in the health care sector. In this chapter, we discuss state-of-the-art deep learning architecture and its optimization when used for medical image segmentation and classification. The chapter closes with a discussion of the challenges of deep learning methods with regard to medical imaging and open research issue.

Subfields: Artificial Intelligence, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/571f4226cb7fbaf2f771fe32c0e4f02b010a223a

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Citations in the corpus

A Review of Deep Learning with Special Emphasis on Architectures, Applications and Recent Trends (2019, background): “hensive Review [245] Deep Learning for Medical Image Processing: Overview, Challenges and the Future [246] An overview of deep learning in medical imaging focusing on MRI [247] Deep Learning in Medical Ultrasound Analysis: A Review [248]”
A review on deep learning in medical image analysis (2021, methodology): “As an open-source deep learning neural network that can run over several CPUs and GPUs, it was publicly released in 2017 [2–4].”
Big Data technologies: A survey (2017, neutral): “Traditional machine-learning techniques and feature engineering algorithms, are limited in their ability to process natural data in their raw form (Razzak et al., 2017).”
U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications (2020, background): “These are some key reasons why deep learning is yet to be used in any large-scale real-world medical trial [377], despite its tremendous promise.”
A Deep Learning-Based Framework for Automatic Brain Tumors Classification Using Transfer Learning (2019, background): “Although deep learning approaches have revealed incredible success in a diversity of applications in numerous domains in different ﬁelds [10,11,13,14,14,16,17,21], nevertheless, it is data starving approach and necessitates as a minimum ten times the degree of freedom data samples.”
Medical image analysis using deep learning algorithms (2023, background): “Moreover, Razzak et al. (33) provided a comprehensive overview of the use of deep learning in medical image processing.”
Uncertainty Sets for Image Classifiers using Conformal Prediction (2020, background): “This is not a casual hypothetical; such classiﬁers are already used in medical settings (e.g., Razzak et al., 2018; Lundervold & Lundervold, 2019; Li et al., 2014).”
DOCToR: The Role of Deep Features in Content-Based Mammographic Image Retrieval (2018, methodology): “This paradigm has been successfully employed in many areas [1], [2], [3].”
Diagnosing COVID-19 pneumonia from x-ray and CT images using deep learning and transfer learning algorithms (2020, methodology): “Further, the RNN is compatible with those applications which are getting sequential data such text [15] and signal reading measurements [16].”
Advancing Medical Imaging Informatics by Deep Learning-Based Domain Adaptation (2020, background): “This situation is worse for rare diseases, where a low number of positive cases lead to significantly unbalanced datasets [10].”
Deep learning and medical image processing for coronavirus (COVID-19) pandemic: A survey (2020, positive): “DL is popularly implemented in the health care sector for broad data interpretations (Razzak, Naz, & Zaib, 2018), aiding early diagnosis of diseases, thereby reducing manual workload.”
Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization (2020, background): “The healthcare sector has not fulfilled the aspirations of society, while the industry absorbs a large percentage of national budgets [12].”
Evaluation of artificial intelligence techniques in disease diagnosis and prediction (2023, background): “Even though various researchers have recently addressed the issue of model complexity in DL [127–130], there is still a need for further investigation and effort in this area [131].”