Version 1
: Received: 20 September 2024 / Approved: 20 September 2024 / Online: 21 September 2024 (00:59:49 CEST)
How to cite:
Younis, E. M.; Mahmoud, M. N.; Albarrak, A. M.; V, I. A. A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images. Preprints2024, 2024091627. https://doi.org/10.20944/preprints202409.1627.v1
Younis, E. M.; Mahmoud, M. N.; Albarrak, A. M.; V, I. A. A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images. Preprints 2024, 2024091627. https://doi.org/10.20944/preprints202409.1627.v1
Younis, E. M.; Mahmoud, M. N.; Albarrak, A. M.; V, I. A. A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images. Preprints2024, 2024091627. https://doi.org/10.20944/preprints202409.1627.v1
APA Style
Younis, E. M., Mahmoud, M. N., Albarrak, A. M., & V, I. A. (2024). A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images. Preprints. https://doi.org/10.20944/preprints202409.1627.v1
Chicago/Turabian Style
Younis, E. M., Abdullah M. Albarrak and Ibrahim A. V. 2024 "A Hybrid Deep Learning Model with Data Augmentation to Improve Tumour Classification Using MRI Images" Preprints. https://doi.org/10.20944/preprints202409.1627.v1
Abstract
Cancer ranks second among the causes of mortality worldwide,following cardiovascular diseases.Brain cancer, in particular, has lowest survival rate of any kind of cancer. Brain tumours vary in their morphology, texture, and location, which determine their classification. Accurate diagnosis of the tumour category enables physicians to select optimal treatment strategies and potentially prolong patients’ lives. Researchers who also implemented deep learning models for diagnosing diseases in recent years largely focused on deep neural network optimization to enhance neural network performance. This implicates implementing models with the best performance and incorporating various network architectures by configuring their hyperparameters. This paper presents a novel hybrid approach method for improved brain tumour classification by combining CNN and EfficientNetV2B3 as feature extraction, followed by (KNN) for classification. To evaluate the recommended method’s efficacy, two widely known benchmark MRI datasets were utilized in the experiments. The initial dataset consisted of 3064 MRI images depicting meningiomas,pituitary,and gliomas tumours.Images from two classes of normal brain and brain tumour were included in the second dataset, which was obtained from Kaggle.In order to enhance performance even more, the study concatenates the CNN and EfficientNetV2B3 flattened outputs before feeding them into the KNN classifier. The proposed framework run on two different dataset and demonstrates outstanding performance with an accuracy of 99.51% and 99.8% on each dataset.
Keywords
Brain tumour; CNN; EfficientNetV2B3; KNN
Subject
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
