4 Impact of Deep Neural Learning on Artificial Intelligence Research

Adate, Amit; Arya, Dhruv; Shaha, Aditya Pankaj; Tripathy, B. K.

doi:10.1515/9783110670905-004

Cited by 15 publications

(1 citation statement)

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“…One of the oldest and most effective models to capture uncertainty in data is artificial neural networks (ANN). After the deep learning idea was introduced to ANN, deep neural network (DNN) models were suggested [1,2]. DL's dense network of layers processes raw inputs both linearly and nonlinearly, layer by layer, in an ordered fashion.…”

Section: Mri (Magnetic Resonance Imagingmentioning

confidence: 99%

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

Khari,

Gupta,

Chaudhary

et al. 2023

EAI Endorsed Trans Perv Health Tech

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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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Section: Mri (Magnetic Resonance Imagingmentioning

confidence: 99%