http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/37 SPS 2026-07-31T08:45:33Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31903 Title: Current challenges of the state-of-the-art of AI techniques for diagnosing brain tumor Authors: Ahmed, Hussaini; Dada, Michael; Samaila, Bulus Abstract: Brain is the control center of the human body, in recent time, different variety of brain diseases are being discovered. The brain disease diagnosis tools are becoming challenging and still an open area of research, application of AI in brain disease diagnosis has made disease prediction and detection more precise and accurate. Automated technologies for non-invasive analysis of brain images have become necessary, because disease of brain is fatal and are the cause of large number of deaths in developed countries. Brain tumor surgery augmented with AI can result in safer and more effective treatment. The knowledge gap between clinical and data science experts still presents significant challenges. This paper will review literatures related to current challenges of AI technologies for brain tumor diagnosis and suggest new directions of AI technologies for diagnosing brain tumour. A systematic search of major academic databases (such as Science Direct, IEEE explore digital Library, and Google scholar) was conducted to identify relevant studies published between 2015 and 2023. The search term used in this study include “Brain tumor Diagnosis”, “AI challenges in Brain tumor Diagnosis”, ‘AI techniques” and “AI challenges in medicine and future”. Studies were included if they utilized AI techniques for brain tumor diagnosis. The identified studies were evaluated for the key challenges they encountered in their diagnostic approaches. The Present study identified several challenges related to the application of AI techniques in brain tumour diagnosis. These challenges include: Interpretability and explainability, variations in tumour location, shape, and size which make accurate segmentation and classification difficult. Overall, the challenges in explaining brain tumor detection stem from the unique requirements and complexities of the healthcare domain, necessitating specialized techniques and approaches. This study summarizes the new directions for AI as (I) Data Hungry: Large, standardized, annotated data sets and excellent ground truth data are necessary for the development of accurate AI. (II) Radiomics: makes it possible to extract a vast number of quantitative features from intricate clinical imaging arrays and convert them into high-dimensional data that can be further processed to determine their relationship to the histological features of the tumor, which represent underlying genetic mutations and malignancy as well as grade, progression, response to therapy, and even overall survival (OS). (III) Black box: AI, for instance, is capable of predicting the best course of care for a patient, but it is unable to explain its reasoning. A trend toward easing this restriction is interpretable deep learning, (IV) Demonstrating the generalizability of deep learning applications and conducting external validation are two major obstacles. (V) There are knowledge gaps in clinical oncology that need to be filled in order to successfully integrate AI and maximize its effects. (VI) Several national professional bodies have started programs to bridge these knowledge gaps and advance the adoption of AI in oncology in response to these difficulties. Description: https://medcraveonline.com/MSEIJ/current-challenges-of-the-state-of-the-art-of-aitechniques-for-diagnosing-brain-tumornbsp.html 2023-12-11T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31902 Title: The performance of machine learning approaches for attenuation correction of PET in neuroimaging: A meta-analysis Authors: Raymond, Confidence; Jurkiewicz, Michael T.; Orunmuyi, Akintunde; Liu, Linshan; Dada, Michael; Ladefoged, Claes; Teuho, Jarmo; Anazodo, Udunna Abstract: Purpose: This systematic review provides a consensus on the clinical feasibility of machine learning (ML) methods for brain PET attenuation correction (AC). Performance of ML-AC were compared to clinical standards. Methods: Two hundred and eighty studies were identified through electronic searches of brain PET studies published between January 1, 2008, and August 1, 2022. Reported outcomes for image quality, tissue classifi cation performance, regional and global bias were extracted to evaluate ML-AC performance. Methodological quality of included studies and the quality of evidence of analysed outcomes were assessed using QUADAS-2 and GRADE, respectively. Results: A total of 19 studies (2371 participants) met the inclusion criteria. Overall, the global bias of ML methods was 0.76 § 1.2%. For image quality, the relative mean square error (RMSE) was 0.20 § 0.4 while for tissues classification, the Dice similarity coefficient (DSC) for bone/soft tissue/air were 0.82 § 0.1 / 0.95 § 0.03 / 0.85 §0.14. Conclusions: In general, ML-AC performance is within acceptable limits for clinical PET imaging. The sparse information on ML-AC robustness and its limited qualitative clinical evaluation may hinder clinical imple mentation in neuroimaging, especially for PET/MRI or emerging brain PET systems where standard AC approaches are not readily available. Description: https://linkinghub.elsevier.com/retrieve/pii/S0150-9861(23)00164-5 2023-02-03T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31901 Title: Effects of Relaxation Times from the Bloch Equations on Age Related Changes in White and Grey Matter Authors: Yusuf, Shakirudeen; Olaoye, Dhikrullah; Dada, Michael; Saba, Ahmed; Audu, Khadeejah; Ibrahim, Jeremiah; Jatto, Abdulsamad Abstract: This research work presented the analytical method of using T1 and T2 relaxation rates of white matter and grey matter to distinguish the passage of time on human organs. A time dependent model equation evolved from the Bloch Nuclear Magnetic Resonance equation was solved under the influence of the radio frequency magnetic field [rfB1(x, t) ̸= 0] and in the absence of radio frequency magnetic field [rfB1(x, t) = 0]. The general solution was considered in three cases. Analysis of the solutions obtained revealed that the rate of decrease of the white matter was faster than that of the grey matter. Between 100 and 400 seconds the difference is more noticeable. Description: https://ijmso.unilag.edu.ng/article/view/2058 2024-02-20T00:00:00Z http://irepo.futminna.edu.ng:8080/jspui/handle/123456789/31900 Title: Multidimensional Correlation Magnetic Resonance Imaging in Low-and Middle-Income Countries: Opportunities and Barriers to Equitable Deployment Authors: Genyi, Terfa; Awojoyogbe, Bamidele; Dada, Michael; Oyeleke, Olarinoye Abstract: Multidimensional Correlation Magnetic Resonance Imaging (McMRI) represents a promising advancement in medical imaging, offering enhanced diagnostic accuracy through the integration of multiple MRI parameters into unified datasets. In low-and middle-income countries (LMICs), McMRI could significantly improve early disease detection and treatment planning, particularly for complex conditions such as cancer and neurological disorders. Benefits include richer data acquisition, improved tissue characterization, and the potential for cost-effective, non-invasive diagnostics. However, practical barriers remain: limited infrastructure, scarcity of trained personnel, high implementation costs, unstable power supply, and data governance challenges hinder widespread adoption. Addressing these issues requires efficient computational workflows, sustainable technology investment, and capacity-building initiatives tailored to LMIC contexts. This review highlights both the transformative potential and the practical obstacles of deploying McMRI in resource-constrained settings, underscoring the need for collaborative strategies that align technological innovation with healthcare equity. Description: https://www.frontiersin.org/journals/public-health/articles/10.3389/fpubh.2026.1870188/abstract 2026-06-22T00:00:00Z