Medical X-ray imaging is a fundamental pillar of musculoskeletal assessment, providing useful information on bone structure, alignment, and potential anomalies. However, such images are generally accompanied by noise, poor contrast, and a number of different types of artifacts, which can mask important anatomical detail and render accurate analysis impossible. These limitations not only affect diagnostic interpretation but also pose challenges to the design and optimization of ergonomic medical devices that rely on precise anatomical data to deliver personalized support. In this article, we review classic image-enhancement techniques alongside modern deep-learning approaches for improving the quality and interpretability of X-ray images. We propose an experimental design workflow that evaluates the performance of several augmentation methods towards the application in intelligent chair design. Further, we present an integrated system architecture that incorporates augmented imaging and AI-driven decision-making modules.
Finally, we consider both objective metrics and observer-rated evaluation criteria to evaluate the performance of the proposed strategies and their capacity to optimize patient care and device function.
Index Terms—X-ray enhancement, CLAHE, Bilateral Filter,Histogram Equalization, Log Correction, Adaptive Threshold,Gaussian Blur, AI, Smart Chair, IoT.

Auteurs : Ali Hamdi , Sawsan Selmi, Hédi Sakli

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Colorectal cancer remains a major public health challenge, and accurate quantification of T lymphocytes (CD3+,CD8+) within the tumor microenvironment holds important prognostic and predictive value. We present MS-HAF-Net, a multiscale hybrid dual-encoder architecture (a convolutional branch based on ResNet34 and a Swin Transformer branch) designed for both semantic segmentation and instance segmentation of lymphocytes in histopathological images. MS-HAF-Net fuses fine
local features with global contextual representations through multi-scale fusion, and is enhanced by a Residual Feature Augmentation (RFA) module and ECA attention blocks to improve detection of very small objects.
The network outputs multi-task heads (masks, contours, and center heatmaps) that enable robust instance separation via watershed post-processing. Comparative experiments against several baselines (YOLOv8-Seg, YOLOv11-Seg, SwinUNet, TransUNet, U-Net+EfficientNetB4) demonstrate that MS-HAF-Net reduces instance merging in dense regions while maintaining high localization accuracy. These results position MS-HAF-Net as a promising solution for integrating automated diagnostic assistance tools in oncology.
Index Terms—instance segmentation, histopathology, lymphocytes, CNN+Transformer, Swin, ResNet, MS-HAF-Net, ECA,RFA.

Auteurs : Feriel Lamirem , Wyssem Fathallah, Imen Helal, Nizar Sakli, Hedia Bellali, Hedi Sakli

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We conduct a comparative evaluation of chest X-ray and computed tomography (CT) scan imaging modalities for machine learning-based automatic COVID-19 diagnosis. Using a public corpus of 17,599 medical images, we applied transfer learning with MobileNetV2 to assess diagnostic accuracy, sensitivity, and specificity and established that CT scans are more accurate than X-rays, yet X-rays are more sensitive to non-COVID cases, and therefore have complementary roles in clinical practice.
Keywords— COVID-19 screening, Medical imaging, Chest X-ray, Computed tomography (CT) scan, Machine learning, Deep learning, Transfer learning, Image reprocessing, Artifact removal, Deblurring, AI-aided diagnosis, Data augmentation,Diagnostic accuracy, Multimodal imaging.

Auteurs : Jawhara Lazhar , Boutheina Ben-Ismail, Wyssem Fathallah, Hedi Sakli

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