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Albalawi E, Thakur A, Dorai DR, Bhatia Khan S, Mahesh TR, Almusharraf A, Aurangzeb K, Anwar MS. Enhancing brain tumor classification in MRI scans with a multi-layer customized convolutional neural network approach. Front Comput Neurosci 2024; 18:1418546. [PMID: 38933391 PMCID: PMC11199693 DOI: 10.3389/fncom.2024.1418546] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/16/2024] [Accepted: 05/23/2024] [Indexed: 06/28/2024] Open
Abstract
Background The necessity of prompt and accurate brain tumor diagnosis is unquestionable for optimizing treatment strategies and patient prognoses. Traditional reliance on Magnetic Resonance Imaging (MRI) analysis, contingent upon expert interpretation, grapples with challenges such as time-intensive processes and susceptibility to human error. Objective This research presents a novel Convolutional Neural Network (CNN) architecture designed to enhance the accuracy and efficiency of brain tumor detection in MRI scans. Methods The dataset used in the study comprises 7,023 brain MRI images from figshare, SARTAJ, and Br35H, categorized into glioma, meningioma, no tumor, and pituitary classes, with a CNN-based multi-task classification model employed for tumor detection, classification, and location identification. Our methodology focused on multi-task classification using a single CNN model for various brain MRI classification tasks, including tumor detection, classification based on grade and type, and tumor location identification. Results The proposed CNN model incorporates advanced feature extraction capabilities and deep learning optimization techniques, culminating in a groundbreaking paradigm shift in automated brain MRI analysis. With an exceptional tumor classification accuracy of 99%, our method surpasses current methodologies, demonstrating the remarkable potential of deep learning in medical applications. Conclusion This study represents a significant advancement in the early detection and treatment planning of brain tumors, offering a more efficient and accurate alternative to traditional MRI analysis methods.
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Affiliation(s)
- Eid Albalawi
- Department of Computer Science, King Faisal University, Al-Ahsa, Saudi Arabia
| | - Arastu Thakur
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - D. Ramya Dorai
- Department of Information Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Surbhi Bhatia Khan
- School of Science, Engineering and Environment, University of Salford, Manchester, United Kingdom
- Department of Electrical and Computer Engineering, Lebanese American University, Byblos, Lebanon
| | - T. R. Mahesh
- Department of Computer Science and Engineering, Faculty of Engineering and Technology, JAIN (Deemed-to-be University), Bangalore, India
| | - Ahlam Almusharraf
- Department of Management, College of Business Administration, Princess Nourah Bint Abdulrahman University, Riyadh, Saudi Arabia
| | - Khursheed Aurangzeb
- Department of Computer Engineering, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia
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Kim HM, Byun SS, Kim JK, Jeong CW, Kwak C, Hwang EC, Kang SH, Chung J, Kim YJ, Ha YS, Hong SH. Machine learning-based prediction model for late recurrence after surgery in patients with renal cell carcinoma. BMC Med Inform Decis Mak 2022; 22:241. [PMID: 36100881 PMCID: PMC9472380 DOI: 10.1186/s12911-022-01964-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2022] [Accepted: 07/21/2022] [Indexed: 11/24/2022] Open
Abstract
Background Renal cell carcinoma is characterized by a late recurrence that occurs 5 years after surgery; hence, continuous monitoring and follow-up is necessary. Prognosis of late recurrence of renal cell carcinoma can only be improved if it is detected early and treated appropriately. Therefore, tools for rapid and accurate renal cell carcinoma prediction are essential. Methods This study aimed to develop a prediction model for late recurrence after surgery in patients with renal cell carcinoma that can be used as a clinical decision support system for the early detection of late recurrence. We used the KOrean Renal Cell Carcinoma database that contains large-scale cohort data of patients with renal cell carcinoma in Korea. From the collected data, we constructed a dataset of 2956 patients for the analysis. Late recurrence and non-recurrence were classified by applying eight machine learning models, and model performance was evaluated using the area under the receiver operating characteristic curve. Results Of the eight models, the AdaBoost model showed the highest performance. The developed algorithm showed a sensitivity of 0.673, specificity of 0.807, accuracy of 0.799, area under the receiver operating characteristic curve of 0.740, and F1-score of 0.609. Conclusions To the best of our knowledge, we developed the first algorithm to predict the probability of a late recurrence 5 years after surgery. This algorithm may be used by clinicians to identify patients at high risk of late recurrence that require long-term follow-up and to establish patient-specific treatment strategies. Supplementary Information The online version contains supplementary material available at 10.1186/s12911-022-01964-w.
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Affiliation(s)
- Hyung Min Kim
- Department of Medical Informatics, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea.,Department of Biomedicine and Health Sciences, College of Medicine, The Catholic University of Korea, Seoul, 06591, Korea
| | - Seok-Soo Byun
- Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Korea
| | - Jung Kwon Kim
- Department of Urology, Seoul National University College of Medicine, Seoul National University Bundang Hospital, Seongnam, 13620, Korea
| | - Chang Wook Jeong
- Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Cheol Kwak
- Department of Urology, Seoul National University College of Medicine, Seoul National University Hospital, Seoul, 03080, Korea
| | - Eu Chang Hwang
- Department of Urology, Chonnam National University Medical School, Gwangju, 61469, Korea
| | - Seok Ho Kang
- Department of Urology, Korea University School of Medicine, Seoul, 02841, Korea
| | - Jinsoo Chung
- Department of Urology, National Cancer Center, Goyang, 10408, Korea
| | - Yong-June Kim
- Department of Urology, Chungbuk National University College of Medicine, Cheongju, 28644, Korea.,Department of Urology, College of Medicine, Chungbuk National University, Cheongju, 28644, Korea
| | - Yun-Sok Ha
- Department of Urology, Kyungpook National University Chilgok Hospital, School of Medicine, Kyungpook National University, Daegu, 41404, Korea
| | - Sung-Hoo Hong
- Department of Urology, Seoul St. Mary's Hospital, College of Medicine, The Catholic University, Seoul, 06591, Korea.
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Sunsuhi G, Albin Jose S. An Adaptive Eroded Deep Convolutional neural network for brain image segmentation and classification using Inception ResnetV2. Biomed Signal Process Control 2022. [DOI: 10.1016/j.bspc.2022.103863] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/01/2022]
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