A Novel Approach to Identify the Brain Tumour Using Convolutional Neural Network

Suraj Khari, Deepa Gupta, Alka Chaudhary, Ruchika Bhatla
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 OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients.
 METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result.
 RESULTS: In our proposed model we got 99% accuracy that is higher than the other results.
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引用次数: 0

Abstract

INTRODUCTION: Determining the possibility that an individual is affected by a tumour is an intricate process in today's modern technological and biological age, when feats are reaching unprecedented levels with every passing second. Machine learning modalities could dramatically enhance the accuracy of diagnosis. OBJECTIVES: Our research makes it feasible to detect tumours early, aiding in early diagnosis, and is a necessity for the curative efforts of cancer patients. METHODS: In our research model Convolutional Neural Network (CNN) was implemented using Jupiter to give an accurate result. RESULTS: In our proposed model we got 99% accuracy that is higher than the other results. CONCLUSION: Our research demonstrates the potential of using machine learning techniques to improve the accuracy and efficiency of medical diagnosis.
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利用卷积神经网络识别脑肿瘤的新方法
导读:在当今的现代技术和生物时代,确定个体受肿瘤影响的可能性是一个复杂的过程,当技术达到前所未有的水平时,每一秒都在过去。机器学习模式可以显著提高诊断的准确性。 目的:我们的研究使早期发现肿瘤成为可能,有助于早期诊断,是癌症患者治疗努力的必要条件。 方法:在我们的研究模型中,使用Jupiter实现卷积神经网络(CNN)来给出准确的结果。 结果:在我们提出的模型中,我们获得了99%的准确率,高于其他结果。 结论:我们的研究展示了使用机器学习技术提高医疗诊断准确性和效率的潜力。
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来源期刊
EAI Endorsed Transactions on Pervasive Health and Technology
EAI Endorsed Transactions on Pervasive Health and Technology Computer Science-Computer Science (miscellaneous)
CiteScore
3.50
自引率
0.00%
发文量
14
审稿时长
10 weeks
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