Journal of Imaging

28 pages, 374 KiB

Open AccessReview

Image Processing Hardware Acceleration—A Review of Operations Involved and Current Hardware Approaches

by Costin-Emanuel Vasile, Andrei-Alexandru Ulmămei and Călin Bîră

J. Imaging 2024, 10(12), 298; https://doi.org/10.3390/jimaging10120298 - 21 Nov 2024

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This review provides an in-depth analysis of current hardware acceleration approaches for image processing and neural network inference, focusing on key operations involved in these applications and the hardware platforms used to deploy them. We examine various solutions, including traditional CPU–GPU systems, custom [...] Read more.

(This article belongs to the Section Image and Video Processing)

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21 pages, 3959 KiB

Open AccessArticle

Multimodal Machine Learning for Predicting Post-Surgery Quality of Life in Colorectal Cancer Patients

by Maryem Rhanoui, Mounia Mikram, Kamelia Amazian, Abderrahim Ait-Abderrahim, Siham Yousfi and Imane Toughrai

J. Imaging 2024, 10(12), 297; https://doi.org/10.3390/jimaging10120297 - 21 Nov 2024

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Abstract

Colorectal cancer is a major public health issue, causing significant morbidity and mortality worldwide. Treatment for colorectal cancer often has a significant impact on patients’ quality of life, which can vary over time and across individuals. The application of artificial intelligence and machine learning techniques has great potential for optimizing patient outcomes by providing valuable insights. In this paper, we propose a multimodal machine learning framework for the prediction of quality of life indicators in colorectal cancer patients at various temporal stages, leveraging both clinical data and computed tomography scan images. Additionally, we identify key predictive factors for each quality of life indicator, thereby enabling clinicians to make more informed treatment decisions and ultimately enhance patient outcomes. Our approach integrates data from multiple sources, enhancing the performance of our predictive models. The analysis demonstrates a notable improvement in accuracy for some indicators, with results for the Wexner score increasing from 24% to 48% and for the Anorectal Ultrasound score from 88% to 96% after integrating data from different modalities. These results highlight the potential of multimodal learning to provide valuable insights and improve patient care in real-world applications. Full article

(This article belongs to the Section Medical Imaging)

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16 pages, 5582 KiB

Open AccessArticle

Evaluating Brain Tumor Detection with Deep Learning Convolutional Neural Networks Across Multiple MRI Modalities

by Ioannis Stathopoulos, Luigi Serio, Efstratios Karavasilis, Maria Anthi Kouri, Georgios Velonakis, Nikolaos Kelekis and Efstathios Efstathopoulos

J. Imaging 2024, 10(12), 296; https://doi.org/10.3390/jimaging10120296 - 21 Nov 2024

Viewed by 94

Abstract

Central Nervous System (CNS) tumors represent a significant public health concern due to their high morbidity and mortality rates. Magnetic Resonance Imaging (MRI) has emerged as a critical non-invasive modality for the detection, diagnosis, and management of brain tumors, offering high-resolution visualization of anatomical structures. Recent advancements in deep learning, particularly convolutional neural networks (CNNs), have shown potential in augmenting MRI-based diagnostic accuracy for brain tumor detection. In this study, we evaluate the diagnostic performance of six fundamental MRI sequences in detecting tumor-involved brain slices using four distinct CNN architectures enhanced with transfer learning techniques. Our dataset comprises 1646 MRI slices from the examinations of 62 patients, encompassing both tumor-bearing and normal findings. With our approach, we achieved a classification accuracy of 98.6%, underscoring the high potential of CNN-based models in this context. Additionally, we assessed the performance of each MRI sequence across the different CNN models, identifying optimal combinations of MRI modalities and neural networks to meet radiologists’ screening requirements effectively. This study offers critical insights into the integration of deep learning with MRI for brain tumor detection, with implications for improving diagnostic workflows in clinical settings. Full article

(This article belongs to the Special Issue Image Segmentation Techniques: Current Status and Future Directions (2nd Edition))

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J. Imaging, Volume 10, Issue 12 (December 2024) – 3 articles

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