Improved Multiclass Brain Tumor Detection using Convolutional Neural Networks and Magnetic Resonance Imaging

被引：0

作者：

Mahjoubi, Mohamed Amine ^{[1
]}

Hamida, Soufiane ^{[1
,2
]}

El Gannour, Oussama ^{[1
]}

Cherradi, Bouchaib ^{[1
,3
]}

El Abbassi, Ahmed ^{[4
]}

Raihani, Abdelhadi ^{[1
]}

机构：

[1] Hassan II Univ Casablanca, ENSET Mohammedia, EEIS Lab, Mohammadia 28830, Morocco

[2] SupMTI Rabat, GENIUS Lab, Rabat, Morocco

[3] STIE Team, Prov Sect Jadida, El Jadida 24000, Settat, Morocco

[4] My Ismail Univ, ERTTI Lab, FST Errachidia, Errachidia, Morocco

来源：

INTERNATIONAL JOURNAL OF ADVANCED COMPUTER SCIENCE AND APPLICATIONS | 2023年 / 14卷 / 03期

关键词：

Deep learning; convolutional neural networks; brain tumor; classification; magnetic resonance imaging; MRI; CLASSIFICATION;

D O I：

10.14569/IJACSA.2023.0140346

中图分类号：

TP301 [理论、方法];

学科分类号：

081202 ;

摘要：

Recently, Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been applied extensively for image recognition and classification tasks, with successful results in the field of medicine, such as in medical image analysis. Radiologists have a hard time categorizing this lethal illness since brain tumors include a variety of tumor cells. Lately, methods based on computer-aided diagnostics claimed to employ magnetic resonance imaging to help with the diagnosis of brain cancers (MRI). Convolutional Neural Networks (CNNs) are often used in medical image analysis, including the detection of brain cancers. This effort was motivated by the difficulty that physicians have in appropriately detecting brain tumors, particularly when they are in the early stages of brain bleeding. This proposed model categorized the brain image into four distinct classes: (Normal, Glioma, Meningioma, and Pituitary). The proposed CNN networks reach 95% of recall, 95.44% accuracy and 95.36% of F1-score.

引用

页码：406 / 414

页数：9