Special Issue "Deep Learning and Data Analytics Techniques for Processing of Biomedical Images"

A special issue of Journal of Imaging (ISSN 2313-433X). This special issue belongs to the section "Medical Imaging".

Deadline for manuscript submissions: 1 August 2023 | Viewed by 1358

Special Issue Editors

Department of Industrial Engineering, Hanyang University, 222 Wangsimini-ro, Seongdong-gu, Seoul 04763, Republic of Korea
Interests: data mining; machine learning; big data analytics; artificial intelligence
Special Issues, Collections and Topics in MDPI journals
Department of CSE, R.M.K Engineering College, Chennai, India
Interests: data science; deep learning and machine learning
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Research on computer analysis of medical images holds great potential for enhancing the health of patients. However, a number of systematic obstacles are impeding the field’s advancement, including data limitations, such as biases, and research incentives, such as optimization for publication. Medical imaging plays a significant role in different clinical applications, such as medical procedures used for early detection, monitoring, diagnosis, and treatment evaluation of various medical conditions. The basics of the principles and implementations of artificial neural networks and deep learning are essential for understanding medical image analysis in computer vision. The deep learning approach (DLA) in medical image analysis has emerged as a fast-growing research field. Deep learning has recently revolutionized medical image computing methods by automating the discovery of features and producing superior results. Recent developments in deep learning have heightened the importance of biomedical signal and image processing research. In order to provide clinicians with useful information, biomedical signal processing requires the analysis of measurements taken at specific points in time and recorded in a patient’s chart. Biomedical image processing is conceptually similar to biomedical signal processing in multiple dimensions. Using X-ray, ultrasound, MRI, nuclear medicine, and visual imaging technologies, it involves image analysis, enhancement, and presentation.

In response, this Special Issue solicits original and novel methodological contributions addressing key challenges in the explainability and generalizability of deep learning for medical imaging. Submissions should emphasize research and advanced development of technical aspects of new image analysis methodologies, and all newly developed methods should be evaluated or validated using real and massive medical imaging data.

Dr. Sathishkumar V E.
Dr. Neelakandan Subramani
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Journal of Imaging is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.


  • deep learning
  • internet of things
  • internet of medical things
  • biomedical image analysis
  • medical image processing
  • medical disease analysis
  • biomedical data analytics
  • multimodal image analysis
  • healthcare data analysis

Published Papers (1 paper)

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Human Hepatocellular Carcinoma Classification from H&E Stained Histopathology Images with 3D Convolutional Neural Networks and Focal Loss Function
J. Imaging 2023, 9(2), 25; https://doi.org/10.3390/jimaging9020025 - 21 Jan 2023
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This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis [...] Read more.
This paper proposes a new Hepatocellular Carcinoma (HCC) classification method utilizing a hyperspectral imaging system (HSI) integrated with a light microscope. Using our custom imaging system, we have captured 270 bands of hyperspectral images of healthy and cancer tissue samples with HCC diagnosis from a liver microarray slide. Convolutional Neural Networks with 3D convolutions (3D-CNN) have been used to build an accurate classification model. With the help of 3D convolutions, spectral and spatial features within the hyperspectral cube are incorporated to train a strong classifier. Unlike 2D convolutions, 3D convolutions take the spectral dimension into account while automatically collecting distinctive features during the CNN training stage. As a result, we have avoided manual feature engineering on hyperspectral data and proposed a compact method for HSI medical applications. Moreover, the focal loss function, utilized as a CNN cost function, enables our model to tackle the class imbalance problem residing in the dataset effectively. The focal loss function emphasizes the hard examples to learn and prevents overfitting due to the lack of inter-class balancing. Our empirical results demonstrate the superiority of hyperspectral data over RGB data for liver cancer tissue classification. We have observed that increased spectral dimension results in higher classification accuracy. Both spectral and spatial features are essential in training an accurate learner for cancer tissue classification. Full article
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