80 research outputs found
A hybrid method for traumatic brain injury lesion segmentation
Traumatic brain injuries are significant effects of disability and loss of life. Physicians employ computed tomography (CT) images to observe the trauma and measure its severity for diagnosis and treatment. Due to the overlap of hemorrhage and normal brain tissues, segmentation methods sometimes lead to false results. The study is more challenging to unitize the AI field to collect brain hemorrhage by involving patient datasets employing CT scans images. We propose a novel technique free-form object model for brain injury CT image segmentation based on superpixel image processing that uses CT to analyzing brain injuries, quite challenging to create a high outstanding simple linear iterative clustering (SLIC) method. The maintains a strategic distance of the segmentation image to reduced intensity boundaries. The segmentation image contains marked red hemorrhage to modify the free-form object model. The contour labelled by the red mark is the output from our free-form object model. We proposed a hybrid image segmentation approach based on the combined edge detection and dilation technique features. The approach diminishes computational costs, and the show accomplished 96.68% accuracy. The segmenting brain hemorrhage images are achieved in the clustered region to construct a free-form object model. The study also presents further directions on future research in this domain
Leveraging Semi-Supervised Graph Learning for Enhanced Diabetic Retinopathy Detection
Diabetic Retinopathy (DR) is a significant cause of blindness globally,
highlighting the urgent need for early detection and effective treatment.
Recent advancements in Machine Learning (ML) techniques have shown promise in
DR detection, but the availability of labeled data often limits their
performance. This research proposes a novel Semi-Supervised Graph Learning SSGL
algorithm tailored for DR detection, which capitalizes on the relationships
between labelled and unlabeled data to enhance accuracy. The work begins by
investigating data augmentation and preprocessing techniques to address the
challenges of image quality and feature variations. Techniques such as image
cropping, resizing, contrast adjustment, normalization, and data augmentation
are explored to optimize feature extraction and improve the overall quality of
retinal images. Moreover, apart from detection and diagnosis, this work delves
into applying ML algorithms for predicting the risk of developing DR or the
likelihood of disease progression. Personalized risk scores for individual
patients are generated using comprehensive patient data encompassing
demographic information, medical history, and retinal images. The proposed
Semi-Supervised Graph learning algorithm is rigorously evaluated on two
publicly available datasets and is benchmarked against existing methods.
Results indicate significant improvements in classification accuracy,
specificity, and sensitivity while demonstrating robustness against noise and
outlie rs.Notably, the proposed algorithm addresses the challenge of imbalanced
datasets, common in medical image analysis, further enhancing its practical
applicability.Comment: 13 pages, 6 figure
A survey, review, and future trends of skin lesion segmentation and classification
The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists to reduce the challenges encountered or associated with manual inspection. This article aims to provide a comprehensive literature survey and review of a total of 594 publications (356 for skin lesion segmentation and 238 for skin lesion classification) published between 2011 and 2022. These articles are analyzed and summarized in a number of different ways to contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (dataset utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria. We intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. We also discuss these dimensions to reveal their current trends based on utilization frequencies. In addition, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. Findings, recommendations, and trends are disclosed to inform future research on developing an automated and robust CAD system for skin lesion analysis
Introducing and assessing the explainable AI (XAI)method: SIDU
Explainable Artificial Intelligence (XAI) has in recent years become a
well-suited framework to generate human understandable explanations of black
box models. In this paper, we present a novel XAI visual explanation algorithm
denoted SIDU that can effectively localize entire object regions responsible
for prediction in a full extend. We analyze its robustness and effectiveness
through various computational and human subject experiments. In particular, we
assess the SIDU algorithm using three different types of evaluations
(Application, Human and Functionally-Grounded) to demonstrate its superior
performance. The robustness of SIDU is further studied in presence of
adversarial attack on black box models to better understand its performance.Comment: Preprint-submitted to Journal of Pattern Recognition (Elsevier
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