Neuro Image Processor For Tumour Detection (NIPTI) using Machine Learning

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Published: Mar 1, 2024
DOI: https://doi.org/10.29070/62qdeh70
Keywords:
Deep learning, convolutional neural networks, brain tumor, classification, magnetic resonance imaging

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Authors

Birendra Kumar Saraswat

Computer Science & Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad,UP,India

Shashikant Katiyar

Computer Science & Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad,UP,India

Ashish Kumar Sharma

Computer Science & Engineering, Raj Kumar Goel Institute of Technology, Ghaziabad,UP,India

Abstract

Deep learning algorithms, particularly Convolutional Neural Networks (CNNs), have been extensively applied to image recognition and classification tasks, achieving significant success in medical image analysis. Radiologists face challenges in accurately diagnosing brain tumours due to the variety of tumour cells. Recently, computer-aided diagnostic methods using magnetic resonance imaging (MRI) have been developed to aid in brain cancer diagnosis. CNNs play a crucial role in medical image analysis, including the detection of brain cancers, helping physicians overcome the difficulties in identifying brain tumours, especially in the early stages of brain haemorrhage. The proposed model categorizes brain images into four classes: Normal, Glioma, Meningioma, and Pituitary. It achieves a recall of 95%, an accuracy of 95.44%, and an F1-score of 95.36%.

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Issue

Vol. 21 No. 1 (2024): Journal of Advances in Science and Technology (JAST)

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