A Novel Approach to Detect Brain Tumor Using CNN model of Deep Learning

被引:0
|
作者
Pardhi, Praful [1 ]
Verma, Navya [1 ]
Loya, Nikunj [1 ]
Agrawal, Kartik [1 ]
机构
[1] Shri Ramdeobaba Coll Engn & Management, Nagpur, India
来源
关键词
Brain Tumour; MRI; Watershed Technique; Image Segmentation;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
A tumor is a mass of tissue generated by the aggregation of aberrant cells that continue to grow, and the brain is the most essential organ in the human body, responsible for controlling and regulating all critical life activities for the body. A brain tumor is either formed in the brain or has migrated. Yet, no reason has been found for developing brain tumors. Though brain tumors are uncommon (approximately 1.8 percent of all reported cancers), the death risk of malignant brain tumors is particularly high due to the tumor's location in the body's most essential organ. To reduce the mortality rate, it is critical to accurately detect brain tumors at an early stage. As a result, we've proposed a computer-assisted radiology method for assessing brain tumors from MRI scans for brain tumor diagnostic management. In this research paper, we developed a model that uses the Watershed technique to segment images, extract features, and then use deep learning to detect cancers with high accuracy.
引用
收藏
页码:127 / 135
页数:9
相关论文
共 50 条
  • [1] Brain Tumor Detection Using a Deep CNN Model
    Ben Brahim, Sonia
    Dardouri, Samia
    Bouallegue, Ridha
    APPLIED COMPUTATIONAL INTELLIGENCE AND SOFT COMPUTING, 2024, 2024
  • [2] Performance Analysis of Glioma Brain Tumor Segmentation Using CNN Deep Learning Approach
    Tamilarasi, M.
    IETE JOURNAL OF RESEARCH, 2023, 69 (05) : 2400 - 2411
  • [3] Deep Learning Framework using CNN for Brain Tumor Classification
    Bhardwaj, Neha
    Sood, Meenakshi
    Gill, S. S.
    2022 5TH INTERNATIONAL CONFERENCE ON MULTIMEDIA, SIGNAL PROCESSING AND COMMUNICATION TECHNOLOGIES (IMPACT), 2022,
  • [4] A Novel Fusion Approach to Detect Brain Tumor Using Machine Learning for MRI Images
    Kaliannan, Srisabarimani
    Rengaraj, Arthi
    Daniel, Alex Prabhu
    TRAITEMENT DU SIGNAL, 2022, 39 (04) : 1363 - 1370
  • [5] A deep learning approach to detect phishing websites using CNN for privacy protection
    Zaimi, Rania
    Hafidi, Mohamed
    Lamia, Mahnane
    INTELLIGENT DECISION TECHNOLOGIES-NETHERLANDS, 2023, 17 (03): : 713 - 728
  • [6] A Novel Approach to Classify Brain Tumor with an Effective Transfer Learning based Deep Learning Model
    Khushi, Hafiz Muhammad Tayyab
    Jaffar, Arfan
    Masood, Tehreem
    Akram, Sheeraz
    BRAZILIAN ARCHIVES OF BIOLOGY AND TECHNOLOGY, 2024, 67
  • [7] Automatic brain tumor detection using CNN transfer learning approach
    Vinayak K. Bairagi
    Pratima Purushottam Gumaste
    Seema H. Rajput
    Medical & Biological Engineering & Computing, 2023, 61 (7) : 1821 - 1836
  • [8] Automatic brain tumor detection using CNN transfer learning approach
    Bairagi, Vinayak K.
    Gumaste, Pratima Purushottam
    Rajput, Seema H.
    Chethan, K. S.
    MEDICAL & BIOLOGICAL ENGINEERING & COMPUTING, 2023, 61 (07) : 1821 - 1836
  • [9] A Novel Hybrid Approach Based on Deep CNN to Detect Glaucoma Using Fundus Imaging
    Mahum, Rabbia
    Rehman, Saeed Ur
    Okon, Ofonime Dominic
    Alabrah, Amerah
    Meraj, Talha
    Rauf, Hafiz Tayyab
    ELECTRONICS, 2022, 11 (01)
  • [10] Brain tumor classification using deep CNN features via transfer learning
    Deepak, S.
    Ameer, P. M.
    COMPUTERS IN BIOLOGY AND MEDICINE, 2019, 111