ABC: Artificial Intelligence for Bladder Cancer grading system

Habibi, Kashayar; Tirdad, Kayvan; Cruz, Alex Dela; Wenger, Kenneth; Mari, Andrea; Kuk, Cynthia; Zlotta, Alexandre R.; Sadeghian, Alireza

doi:10.1016/j.mlwa.2022.100387

Cited by 3 publications

(1 citation statement)

References 30 publications

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“…The application of AI in medical science and health care is spreading rapidly. Common domains of applications in medical image analysis include different fields such as organ localization and abnormality detection [ 3 , 4 ], gene mutation detection [ 5 ], and cancer grading [ 6 ] and staging [ 7 ], and the domains are too numerous to mention and still flourishing. Remarkably, AI methods have recently played a significant role in the COVID-19 pandemic as a tool for diagnosis and severity ranking from medical images [ 8 , 9 , 10 ].…”

Section: Introductionmentioning

confidence: 99%

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

et al. 2022

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Human skin diseases have become increasingly prevalent in recent decades, with millions of individuals in developed countries experiencing monkeypox. Such conditions often carry less obvious but no less devastating risks, including increased vulnerability to monkeypox, cancer, and low self-esteem. Due to the low visual resolution of monkeypox disease images, medical specialists with high-level tools are typically required for a proper diagnosis. The manual diagnosis of monkeypox disease is subjective, time-consuming, and labor-intensive. Therefore, it is necessary to create a computer-aided approach for the automated diagnosis of monkeypox disease. Most research articles on monkeypox disease relied on convolutional neural networks (CNNs) and using classical loss functions, allowing them to pick up discriminative elements in monkeypox images. To enhance this, a novel framework using Al-Biruni Earth radius (BER) optimization-based stochastic fractal search (BERSFS) is proposed to fine-tune the deep CNN layers for classifying monkeypox disease from images. As a first step in the proposed approach, we use deep CNN-based models to learn the embedding of input images in Euclidean space. In the second step, we use an optimized classification model based on the triplet loss function to calculate the distance between pairs of images in Euclidean space and learn features that may be used to distinguish between different cases, including monkeypox cases. The proposed approach uses images of human skin diseases obtained from an African hospital. The experimental results of the study demonstrate the proposed framework’s efficacy, as it outperforms numerous examples of prior research on skin disease problems. On the other hand, statistical experiments with Wilcoxon and analysis of variance (ANOVA) tests are conducted to evaluate the proposed approach in terms of effectiveness and stability. The recorded results confirm the superiority of the proposed method when compared with other optimization algorithms and machine learning models.

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Section: Introductionmentioning

confidence: 99%

An Al-Biruni Earth Radius Optimization-Based Deep Convolutional Neural Network for Classifying Monkeypox Disease

et al. 2022

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A review of Artificial Intelligence methods in bladder cancer: segmentation, classification, and detection

Bashkami,

Nasayreh,

Makhadmeh

et al. 2024

Artif Intell Rev

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Artificial intelligence (AI) and other disruptive technologies can potentially improve healthcare across various disciplines. Its subclasses, artificial neural networks, deep learning, and machine learning, excel in extracting insights from large datasets and improving predictive models to boost their utility and accuracy. Though research in this area is still in its early phases, it holds enormous potential for the diagnosis, prognosis, and treatment of urological diseases, such as bladder cancer. The long-used nomograms and other classic forecasting approaches are being reconsidered considering AI’s capabilities. This review emphasizes the coming integration of artificial intelligence into healthcare settings while critically examining the most recent and significant literature on the subject. This study seeks to define the status of AI and its potential for the future, with a special emphasis on how AI can transform bladder cancer diagnosis and treatment.

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