FT-FEDTL: A fine-tuned feature-extracted deep transfer learning model for multi-class microwave-based brain tumor classification

学习迁移 人工智能 班级(哲学) 特征(语言学) 微波食品加热 计算机科学 模式识别(心理学) 深度学习 传输(计算) 机器学习 并行计算 电信 哲学 语言学
作者
Amran Hossain,Rafiqul Islam,Mohammad Tariqul Islam,Phumin Kirawanich,Mohamed S. Soliman
出处
期刊:Computers in Biology and Medicine [Elsevier BV]
卷期号:183: 109316-109316
标识
DOI:10.1016/j.compbiomed.2024.109316
摘要

The microwave brain imaging (MBI) system is an emerging technology used to detect brain tumors in their early stages. Multi-class microwave-based brain tumor (MBT) identification and classification are crucial due to the tumor's patterns and shape. Manual identification and categorization of the tumors from the images by physicians is a challenging task and consumes more time. Recently, to overcome these issues, the deep transfer learning (DTL) technique has been used to classify brain tumors efficiently. This paper proposes a Fine-tuned Feature Extracted Deep Transfer Learning Model called FT-FEDTL for multi-class MBT classification purposes. The main objective of this work is to suggest a better pathway for brain tumor diagnosis by designing an efficient DTL model that automatically identifies and categorizes the MBT images. The InceptionV3 architecture is utilized as a base for feature extraction in the proposed FT-FEDTL model. Thereafter, a fine-tuning method is applied to the additional five layers with hyperparameters. The fine-tuned layers are attached to the base model to enhance classification performance. The MBT data are collected from two sources and balanced by augmentation techniques to create a total of 4200 balanced datasets. Later, 80 % images are used for training, 20 % images are utilized for validation, and 80 samples of each class are used for testing the FT-FEDTL model for classifying tumors into six classes. We evaluated and compared the FT-FEDTL model with the three traditional non-CNN and seven pretrained models by applying an imbalanced and balanced dataset. The proposed model showed superior classification performance compared to other models for the balanced dataset. It attained an overall accuracy, recall, precision, specificity, and Fscore of 99.65 %, 99.16 %, 99.48 %, 99.10 %, and 99.23 %, respectively. The experimental outcomes ensure that the proposed model can be employed in biomedical applications to assist radiologists for multi-class MBT image classification purposes. The Anaconda distribution platform with Python 3.7 on the Windows 11 OS is used to implement the models.
最长约 10秒,即可获得该文献文件

科研通智能强力驱动
Strongly Powered by AbleSci AI
更新
PDF的下载单位、IP信息已删除 (2025-6-4)

科研通是完全免费的文献互助平台,具备全网最快的应助速度,最高的求助完成率。 对每一个文献求助,科研通都将尽心尽力,给求助人一个满意的交代。
实时播报
小2完成签到,获得积分10
刚刚
刚刚
xiaoyan发布了新的文献求助10
刚刚
qizhixu发布了新的文献求助10
1秒前
1秒前
nietao完成签到,获得积分10
2秒前
偷喝汽水给偷喝汽水的求助进行了留言
2秒前
2秒前
YamDaamCaa应助wen采纳,获得60
3秒前
彪yu发布了新的文献求助10
3秒前
4秒前
烟花应助聪明的语风采纳,获得10
4秒前
爱吃饼干的土拨鼠完成签到,获得积分10
5秒前
香蕉觅云应助dong采纳,获得10
5秒前
元谷雪发布了新的文献求助10
5秒前
6秒前
7秒前
量子星尘发布了新的文献求助10
7秒前
刻苦冰颜发布了新的文献求助10
8秒前
8秒前
queen814完成签到,获得积分10
8秒前
9秒前
兰天完成签到,获得积分10
9秒前
陌路孤星完成签到,获得积分10
9秒前
xiaoyuan发布了新的文献求助10
10秒前
开朗的寄灵发布了新的文献求助150
10秒前
tyy发布了新的文献求助10
11秒前
11秒前
儒雅谷芹发布了新的文献求助10
12秒前
12秒前
机智思真发布了新的文献求助10
12秒前
yufanhui应助Wangyingjie5采纳,获得10
12秒前
ED应助_Charmo采纳,获得10
12秒前
666完成签到,获得积分10
12秒前
乔迎晓发布了新的文献求助10
13秒前
celine123发布了新的文献求助10
14秒前
14秒前
14秒前
从容的春天完成签到,获得积分10
14秒前
王心茹完成签到,获得积分20
14秒前
高分求助中
Picture Books with Same-sex Parented Families: Unintentional Censorship 700
ACSM’s Guidelines for Exercise Testing and Prescription, 12th edition 500
Nucleophilic substitution in azasydnone-modified dinitroanisoles 500
不知道标题是什么 500
Indomethacinのヒトにおける経皮吸収 400
Phylogenetic study of the order Polydesmida (Myriapoda: Diplopoda) 370
Effective Learning and Mental Wellbeing 300
热门求助领域 (近24小时)
化学 材料科学 医学 生物 工程类 有机化学 生物化学 物理 内科学 纳米技术 计算机科学 化学工程 复合材料 遗传学 基因 物理化学 催化作用 冶金 细胞生物学 免疫学
热门帖子
关注 科研通微信公众号,转发送积分 3974358
求助须知:如何正确求助?哪些是违规求助? 3518706
关于积分的说明 11195521
捐赠科研通 3254897
什么是DOI,文献DOI怎么找? 1797614
邀请新用户注册赠送积分活动 877011
科研通“疑难数据库(出版商)”最低求助积分说明 806128