Evolutionary Model for Brain Cancer-Grading and Classification

Ullah, Faizan; Nadeem, Muhammad; Abrar, Muhammad; Amin, Farhan; Salam, Abdu; Alabrah, Amerah; AlSalman, Hussain

doi:10.1109/access.2023.3330919

Cited by 12 publications

(2 citation statements)

References 28 publications

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“…A chi-square test for feature selection with gray-level cooccurrence matrix (GLCM) texture characteristics allowed the SVM to perform better in [ 19 ]. A lightweight ensemble model in [ 21 ] incorporates numerous XGBoost decision trees to detect brain cancer from MRI images. The authors extracted intensity, texture, and shape features from the images to classify four grades of patients and obtained 93% accuracy.…”

Section: Literature Reviewmentioning

confidence: 99%

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

Md. Ashafuddula,

Islam

2024

International Journal of Biomedical Imaging

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Brain tumors are critical neurological ailments caused by uncontrolled cell growth in the brain or skull, often leading to death. An increasing patient longevity rate requires prompt detection; however, the complexities of brain tissue make early diagnosis challenging. Hence, automated tools are necessary to aid healthcare professionals. This study is particularly aimed at improving the efficacy of computerized brain tumor detection in a clinical setting through a deep learning model. Hence, a novel thresholding-based MRI image segmentation approach with a transfer learning model based on contour (ContourTL-Net) is suggested to facilitate the clinical detection of brain malignancies at an initial phase. The model utilizes contour-based analysis, which is critical for object detection, precise segmentation, and capturing subtle variations in tumor morphology. The model employs a VGG-16 architecture priorly trained on the “ImageNet” collection for feature extraction and categorization. The model is designed to utilize its ten nontrainable and three trainable convolutional layers and three dropout layers. The proposed ContourTL-Net model is evaluated on two benchmark datasets in four ways, among which an unseen case is considered as the clinical aspect. Validating a deep learning model on unseen data is crucial to determine the model’s generalization capability, domain adaptation, robustness, and real-world applicability. Here, the presented model’s outcomes demonstrate a highly accurate classification of the unseen data, achieving a perfect sensitivity and negative predictive value (NPV) of 100%, 98.60% specificity, 99.12% precision, 99.56% F1-score, and 99.46% accuracy. Additionally, the outcomes of the suggested model are compared with state-of-the-art methodologies to further enhance its effectiveness. The proposed solution outperforms the existing solutions in both seen and unseen data, with the potential to significantly improve brain tumor detection efficiency and accuracy, leading to earlier diagnoses and improved patient outcomes.

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Section: Literature Reviewmentioning

confidence: 99%

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

Md. Ashafuddula,

Islam

2024

International Journal of Biomedical Imaging

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“…However, there is a notable gap in the development of framework tools for the integration of Federated Learning (FL) and Knowledge Distillation (KD) specifically tailored to medical image segmentation. FKD-Med's clinical scenarios underscore its practical relevance, showcasing the potential for application across diverse healthcare settings [16] [17].…”

Section: Introductionmentioning

confidence: 99%

FKD-Med: Privacy-Aware, Communication-Optimized Medical Image Segmentation via Federated Learning and Model Lightweighting Through Knowledge Distillation

Sun,

Shu,

Shao

et al. 2024

IEEE Access

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Advances in deep learning have revolutionized medical image segmentation, facilitating the precise delineation of complex anatomical structures. The scarcity of annotated training samples remains a significant bottleneck. To tackle the data limitation, federated learning (FL) offers the promise of pooling data from multiple healthcare institutions. However, as models grow larger, the increase in communication costs restricts FL to fewer nodes, which constrains the volume of data. This situation necessitates the simultaneous achievement of model lightweighting. To address this problem, this study proposes FKD-Med, a novel framework that integrates FL for privacy-sensitive data amalgamation across multiple healthcare institutions, and uses knowledge distillation (KD) to enhance communication efficiency. The "Med" in FKD-Med refers to medical application computational problems. Our principal contributions encompass the design of an open-source framework that seamlessly blends FL and KD, rendering it applicable to a broad spectrum of medical informatics tasks. Our approach substantially augments the computational data volume, thereby boosting both communication efficiency and training throughput. Tested on two datasets of medical image segmentation using TransUNet and ResUNet as teacher models, FKD-Med achieves data privacy, lowers communication costs, and increases accuracy. The parameters of the student models were reduced to 1/127 and 1/1027 of those in the teacher models. Additionally, the models subjected to KD exhibited accuracy improvements of 0.25%, 0.43%, 1.35%, and 1.46% respectively, given the same parameter volume. This positions FKD-Med not only as a pivotal tool for multi-institutional medical research but also as a versatile platform adaptable to a wide array of real-world medical engineering applications. The code is publicly available at https://github.com/SUN-1024/FKD-Med.

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Improved Glioma Grade Prediction with Mean Image Transformation

Yathirajam,

Gutta

2024

Artificial Intelligence in Medicine

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Evolutionary Model for Brain Cancer-Grading and Classification

Cited by 12 publications

References 28 publications

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

ContourTL-Net: Contour-Based Transfer Learning Algorithm for Early-Stage Brain Tumor Detection

FKD-Med: Privacy-Aware, Communication-Optimized Medical Image Segmentation via Federated Learning and Model Lightweighting Through Knowledge Distillation

Improved Glioma Grade Prediction with Mean Image Transformation

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