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Microscopy research and technique
Jun, 2021;84 (6): 1296-1308.

Brain tumor detection and multi-classification using advanced deep learning techniques.

Tariq Sadad, Amjad Rehman, Asim Munir, Tanzila Saba, Usman Tariq, Noor Ayesha, Rashid Abbasi
Author Information
  1. Tariq Sadad: Department of Computer Science, University of Central Punjab, Lahore, Pakistan. ORCID
  2. Amjad Rehman: Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia. ORCID
  3. Asim Munir: Department of Computer Science and Software Engineering, International Islamic University, Islamabad, Pakistan.
  4. Tanzila Saba: Artificial Intelligence & Data Analytics Lab, CCIS Prince Sultan University, Riyadh, Saudi Arabia. ORCID
  5. Usman Tariq: College of Computer Engineering and Science, Prince Sattam bin Abdulaziz University, Alkharj, Saudi Arabia. ORCID
  6. Noor Ayesha: School of Clinical Medicine, Zhengzhou University, Zhengzhou, Henan, China. ORCID
  7. Rashid Abbasi: School of Information and Communication Engineering, University of Electronic Science and Technology of China, Chengdu, Sichuan, China.
PMID: 33400339 DOI: 10.1002/jemt.23688

Abstract

A brain tumor is an uncontrolled development of brain cells in brain cancer if not detected at an early stage. Early brain tumor diagnosis plays a crucial role in treatment planning and patients' survival rate. There are distinct forms, properties, and therapies of brain tumors. Therefore, manual brain tumor detection is complicated, time-consuming, and vulnerable to error. Hence, automated computer-assisted diagnosis at high precision is currently in demand. This article presents segmentation through Unet architecture with ResNet50 as a backbone on the Figshare data set and achieved a level of 0.9504 of the intersection over union (IoU). The preprocessing and data augmentation concept were introduced to enhance the classification rate. The multi-classification of brain tumors is performed using evolutionary algorithms and reinforcement learning through transfer learning. Other deep learning methods such as ResNet50, DenseNet201, MobileNet V2, and InceptionV3 are also applied. Results thus obtained exhibited that the proposed research framework performed better than reported in state of the art. Different CNN, models applied for tumor classification such as MobileNet V2, Inception V3, ResNet50, DenseNet201, NASNet and attained accuracy 91.8, 92.8, 92.9, 93.1, 99.6%, respectively. However, NASNet exhibited the highest accuracy.

Keywords

NASNet WHO brain tumor cancer health risks healthcare

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MeSH Term

Brain
Brain Neoplasms
Deep Learning
Early Detection of Cancer
Humans
Neural Networks, Computer
Journal Article
Article has an altmetric score of 2

Word Cloud

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