Brain tumors pose significant risks to cognitive functions, making early detection crucial for improving patient survival rates. Accurate tumor detection aids neuro-oncologists in diagnosing tumor types and recommending appropriate treatments. However, manual detection is challenging, time-consuming, and prone to human error. Recently, Deep Learning (DL) models have demonstrated substantial potential in efficiently classifying large image datasets. This paper presents three novel and efficient multi-class DL architectures leveraging transfer learning to classify different brain tumors. Our primary contribution is to enhance the predictive accuracy while minimizing the usage of computational resource compared to other state-of-art models in the literature by forgoing preprocessing, segmentation, hybrid models, and augmentation techniques. Additionally, we reduce the number of FC layers to streamline computation. We conduct extensive experiments to evaluate the performance of our models using the brain tumor Figshare T1-weighted contrast-enhanced MRI dataset, comprising 3064 images from three distinct tumor types. The InceptionV3 model records a 98.36% accuracy level with five-fold cross-validation. By incorporating batch normalization and optimizing the learning rate for the Adam optimizer, the Xception model reached 99.19% accuracy. Finally, we utilize Particle Swarm Optimization (PSO) to fine-tune the learning rate of the Stochastic Gradient Descent (SGD) optimizer. The Xception model attained 98.85% accuracy. These results highlight the novelty of our approach, offering a practical solution for neuro-oncologists, particularly through the fine-tuned Xception model, which delivers early and accurate tumor detection with minimal computational resources.

Wael Korani

Shyam Sundar Domakonda

Priyan Malarvizhi Kumar

Biomedical Engineering Applications Basis and Communications

Biomedical Engineering: Applications, Basis and Communications is an international, interdisciplinary journal aiming at publishing up-to-date contributions on original clinical and basic research in the biomedical engineering. Research of biomedical engineering has grown tremendously in the past few decades. Meanwhile, several outstanding journals in the field have emerged, with different emphases and objectives. We hope this journal will serve as a new forum for both scientists and clinicians to share their ideas and the results of their studies.
Biomedical Engineering: Applications, Basis and Communications explores all facets of biomedical engineering, with emphasis on both the clinical and scientific aspects of the study. It covers the fields of bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences and clinical engineering. The journal fulfils this aim by publishing regular research / clinical articles, short communications, technical notes and review papers. Papers from both basic research and clinical investigations will be considered.

Biomedical Engineering - Applications, Basis and Communications is an international, interdisciplinary journal dedicated to publishing the latest contributions in original clinical and basic research within the field of biomedical engineering. The research in biomedical engineering has grown significantly in recent decades, leading to the emergence of several outstanding journals, each with distinct emphases and objectives. We hope this journal will serve as a new platform for both scientists and clinicians to share their ideas and research findings.

Biomedical Engineering - Applications, Basis and Communications explores all aspects of biomedical engineering, with a focus on both clinical and scientific dimensions of the field. It covers areas such as bioelectronics, biomaterials, biomechanics, bioinformatics, nano-biological sciences, and clinical engineering. The journal fulfills its mission by publishing regular research/clinical articles, short communications, technical notes, and review papers. Papers from both basic research and clinical investigations are welcome.

《Biomedical Engineering - Applications, Basis and Communications》是一本国际性跨学科期刊，旨在发表生物医学工程领域最新的原创临床和基础研究成果。近年来，生物医学工程研究迅速发展，许多优秀的相关期刊相继涌现，各自有不同的侧重点和目标。我们希望本期刊能成为科学家和临床医生分享思想和研究成果的新平台。

《Biomedical Engineering - Applications, Basis and Communications》探讨生物医学工程的各个方面，重点关注研究的临床和科学层面。期刊涵盖生物电子学、生物材料、生物力学、生物信息学、纳米生物科学和临床工程等领域。为实现这一目标，期刊定期发表研究/临床文章、简短通讯、技术笔记和综述论文。我们将考虑基础研究和临床研究的论文。

EARLY DETECTION OF BRAIN TUMOR USING MRI AND TRANSFER LEARNING

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Journal Info

Category	Quartile
ENGINEERING, BIOMEDICAL	4