Employing deep learning and transfer learning for accurate brain tumor detection

Mathivanan, Sandeep Kumar; Sonaimuthu, Sridevi; Murugesan, Sankar; Rajadurai, Hariharan; Shivahare, Basu Dev; Shah, Mohd Asif

doi:10.1038/s41598-024-57970-7

Cited by 13 publications

(2 citation statements)

References 40 publications

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“…Recent advancements in machine learning have led to the development of sophisticated image classification models that can accurately identify different types of brain tumors. Convolutional neural networks (CNNs) are at the forefront of these developments, with architectures designed to process and analyze MRI images for tumor detection [ 12 - 14 ]. A CNN model with four convolutional layers, ReLU activation functions, dropout layers, and max-pooling layers was proposed, achieving an accuracy of 97.39% and an average F1-Score of 96.11% in one test [ 12 ].…”

Section: Introductionmentioning

confidence: 99%

“…By using pre-trained models such as EfficientNets and MobileNetv3, researchers have been able to achieve significant performance improvements. For example, EfficientNetB2 yielded an overall test accuracy of 99.06%, while MobileNetv3 achieved the highest accuracy of 99.75% [ 13 , 14 ]. Transfer learning is particularly beneficial when dealing with limited labeled medical data, as it allows the use of knowledge acquired from extensive benchmark datasets like ImageNet [ 13 , 14 ].…”

Section: Introductionmentioning

confidence: 99%

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The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

Abraham,

Jose,

Farooqui

et al. 2024

Cureus

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This research study explores of the effectiveness of a machine learning image classification model in the accurate identification of various types of brain tumors. The types of tumors under consideration in this study are gliomas, meningiomas, and pituitary tumors. These are some of the most common types of brain tumors and pose significant challenges in terms of accurate diagnosis and treatment.The machine learning model that is the focus of this study is built on the Google Teachable Machine platform (Alphabet Inc., Mountain View, CA). The Google Teachable Machine is a machine learning image classification platform that is built from Tensorflow, a popular open-source platform for machine learning. The Google Teachable Machine model was specifically evaluated for its ability to differentiate between normal brains and the aforementioned types of tumors in MRI images. MRI images are a common tool in the diagnosis of brain tumors, but the challenge lies in the accurate classification of the tumors. This is where the machine learning model comes into play. The model is trained to recognize patterns in the MRI images that correspond to the different types of tumors. The performance of the machine learning model was assessed using several metrics. These include precision, recall, and F1 score. These metrics were generated from a confusion matrix analysis and performance graphs. A confusion matrix is a table that is often used to describe the performance of a classification model. Precision is a measure of the model's ability to correctly identify positive instances among all instances it identified as positive. Recall, on the other hand, measures the model's ability to correctly identify positive instances among all actual positive instances. The F1 score is a measure that combines precision and recall providing a single metric for model performance. The results of the study were promising.The Google Teachable Machine model demonstrated high performance, with accuracy, precision, recall, and F1 scores ranging between 0.84 and 1.00. This suggests that the model is highly effective in accurately classifying the different types of brain tumors. This study provides insights into the potential of machine learning models in the accurate classification of brain tumors. The findings of this study lay the groundwork for further research in this area and have implications for the diagnosis and treatment of brain tumors. The study also highlights the potential of machine learning in enhancing the field of medical imaging and diagnosis. With the increasing complexity and volume of medical data, machine learning models like the one evaluated in this study could play a crucial role in improving the accuracy and efficiency of diagnoses. Furthermore, the study underscores the importance of continued research and development in this field to further refine these models and overcome any potential limitations or challenges. Overall, the study contributes to the field of medical imaging and machine learning and sets the stage for future ...

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

Abraham,

Jose,

Farooqui

et al. 2024

Cureus

View full text Add to dashboard Cite

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3T to 7T Whole Brain + Skull MRI Translation with Densely Engineered U-Net Network

Kalluvila,

Rosen

2024

Artificial Intelligence in Medicine

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Multistage transfer learning for medical images

Ayana,

Dese,

Abagaro

et al. 2024

Artif Intell Rev

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Deep learning is revolutionizing various domains and significantly impacting medical image analysis. Despite notable progress, numerous challenges remain, necessitating the refinement of deep learning algorithms for optimal performance in medical image analysis. This paper explores the growing demand for precise and robust medical image analysis by focusing on an advanced deep learning technique, multistage transfer learning. Over the past decade, multistage transfer learning has emerged as a pivotal strategy, particularly in overcoming challenges associated with limited medical data and model generalization. However, the absence of well-compiled literature capturing this development remains a notable gap in the field. This exhaustive investigation endeavors to address this gap by providing a foundational understanding of how multistage transfer learning approaches confront the unique challenges posed by insufficient medical image datasets. The paper offers a detailed analysis of various multistage transfer learning types, architectures, methodologies, and strategies deployed in medical image analysis. Additionally, it delves into intrinsic challenges within this framework, providing a comprehensive overview of the current state while outlining potential directions for advancing methodologies in future research. This paper underscores the transformative potential of multistage transfer learning in medical image analysis, providing valuable guidance to researchers and healthcare professionals.

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Employing deep learning and transfer learning for accurate brain tumor detection

Cited by 13 publications

References 40 publications

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

The Role of ArtificiaI Intelligence in Brain Tumor Diagnosis: An Evaluation of a Machine Learning Model

3T to 7T Whole Brain + Skull MRI Translation with Densely Engineered U-Net Network

Multistage transfer learning for medical images

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