A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: A design and implementation study

Hosseini, Azamossadat; Eshraghi, Mohammad Amir; Taami, Tania; Sadeghsalehi, Hamidreza; Hoseinzadeh, Zahra; Ghaderzadeh, Mustafa; Rafiee, Mohammad

doi:10.1016/j.imu.2023.101244

Cited by 34 publications

(18 citation statements)

References 6 publications

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“…For review of biomedical-related deep learning, existing concepts, and alternative methods see e.g. [60][61][62][63]. Note that while this study introduces MDOF biosystem sub-asymptotic technique, the EVT approach being asymptotic and…”

Section: Resultsmentioning

confidence: 99%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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Background Prevalence of dementia illness, causing certain morbidity and mortality globally, places burden on global public health. This study primary goal was to assess future risks of dying from severe dementia, given specific return period, within selected group of regions or nations. Methods Traditional statistical approaches do not have benefits of effectively handling large regional dimensionality, along with nonlinear cross-correlations between various regional observations. In order to produce reliable long-term projections of excessive dementia death rate risks, this study advocates novel bio-system reliability technique, that being particularly suited for multi-regional environmental, biological, and health systems. Data Raw clinical data has been used as an input to the suggested population-based, bio-statistical technique using data from medical surveys and several centers. Results Novel spatiotemporal health system reliability methodology has been developed and applied to dementia death rates raw clinical data. Suggested methodology shown to be capable of dealing efficiently with spatiotemporal clinical observations of multi-regional nature. Accurate disease risks multi-regional spatiotemporal prediction being done, relevant confidence intervals have been presented as well. Conclusions Based on available clinical survey dataset, the proposed approach may be applied in a variety of clinical public health applications. Confidence bands, given for predicted dementia-associated death rate levels with return periods of interest, have been reasonably narrow, indicating practical values of advocated prognostics.

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Section: Resultsmentioning

confidence: 99%

Dementia death rates prediction

Gaidai,

Yakimov,

Balakrishna

2023

BMC Psychiatry

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“…CDSSs that increase evidence-based decision making in healthcare have the potential to improve quality of care and patient health and outcomes, but will only be of benefit if used wisely in the clinical environments (23,25). The tension between the evidence that the CDSS improves medical decision making and physicians' perceptions that it may not always do so, is needed.…”

Section: Model Summary Anovamentioning

confidence: 99%

Advancing acceptance: assessing acceptance of the ESR iGuide clinical decision support system for improved computed tomography test justification

Singer,

Luxenburg,

Rosen

et al. 2023

Front. Med.

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BackgroundA clinical decision support system (CDSS), the European Society of Radiologists (ESR) iGuide, was developed to address gaps in the availability and use of effective imaging referral guidelines.AimThis study aimed to assess the appropriateness of computed tomography (CT) exams with and without ESR iGuide use, as well as the usability and acceptance of the physician systems.MethodsA retrospective single-center study was conducted in which data from 278 consecutive CT tests referred by physicians were collected in the first phase (T1), and physicians used the ESR iGuide system for imaging referrals in the second phase (T2; n = 85). The appropriateness of imaging referrals in each phase was assessed by two experts, and physicians completed the System Usability Scale.ResultsThe mean appropriateness level on a scale of 0–9 was 6.62 ± 2.69 at T1 and 7.88 ± 1.4 at T2. When using a binary variable (0–6 = non-appropriate; 7–9 = appropriate), 70.14% of cases were found appropriate at T1 and 96.47% at T2. Surgery physician specialty and post-intervention phase showed a higher likelihood of ordering an appropriate test (p = 0.0045 and p = 0.0003, respectively). However, the questionnaire results indicated low system trust and minimal clinical value, with all physicians indicating they would not recommend collegial use (100%).ConclusionThe study suggests that ESR iGuide can effectively guide the selection of appropriate imaging tests. However, physicians showed low system trust and use, indicating a need for further understanding of CDSS acceptance properties. Maximizing CDSS potential could result in crucial decision-support compliance and promotion of appropriate imaging.

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“…A segmented picture was created by performing the K-means clustering technique and then adding a mask on the images that were clustered. This allowed for the removal of components that were not essential [ 21 ].…”

Section: Introductionmentioning

confidence: 99%

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images

Dhanagopal,

Menaka,

Suresh Kumar

et al. 2024

Journal of Healthcare Engineering

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Osteoporosis is a word used to describe a condition in which bone density has been diminished as a result of inadequate bone tissue development to counteract the elimination of old bone tissue. Osteoporosis diagnosis is made possible by the use of medical imaging technologies such as CT scans, dual X-ray, and X-ray images. In practice, there are various osteoporosis diagnostic methods that may be performed with a single imaging modality to aid in the diagnosis of the disease. The proposed study is to develop a framework, that is, to aid in the diagnosis of osteoporosis which agrees to all of these CT scans, X-ray, and dual X-ray imaging modalities. The framework will be implemented in the near future. The proposed work, CBTCNNOD, is the integration of 3 functional modules. The functional modules are a bilinear filter, grey-level zone length matrix, and CB-CNN. It is constructed in a manner that can provide crisp osteoporosis diagnostic reports based on the images that are fed into the system. All 3 modules work together to improve the performance of the proposed approach, CBTCNNOD, in terms of accuracy by 10.38%, 10.16%, 7.86%, and 14.32%; precision by 11.09%, 9.08%, 10.01%, and 16.51%; sensitivity by 9.77%, 10.74%, 6.20%, and 12.78%; and specificity by 11.01%, 9.52%, 9.5%, and 15.84%, while requiring less processing time of 33.52%, 17.79%, 23.34%, and 10.86%, when compared to the existing techniques of RCETA, BMCOFA, BACBCT, and XSFCV, respectively.

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A mobile application based on efficient lightweight CNN model for classification of B-ALL cancer from non-cancerous cells: A design and implementation study

Cited by 34 publications

References 6 publications

Dementia death rates prediction

Dementia death rates prediction

Advancing acceptance: assessing acceptance of the ESR iGuide clinical decision support system for improved computed tomography test justification

Channel-Boosted and Transfer Learning Convolutional Neural Network-Based Osteoporosis Detection from CT Scan, Dual X-Ray, and X-Ray Images

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