NEW EFFICIENCY SYSTEM FOR BRAIN TUMOR CLASSIFICATION USING MACHINE LEARNING ALGORITHM



Authors

  • 1Narakuchi Venkata Naga Sai Akanksha 2Dr. P Harini 3N Lakshmi Narayana

DOI:

https://doi.org/10.15282/jmes.17.1.2023.10.0759


Keywords:

Brain Tumor Detection, MRI, CT scan, Deep neural networks, convolution neural network, segmentation, classification, Brain imaging modalities · Segmentation · Feature extraction


Abstract

Brain tumor occurs owing to uncontrolled and rapid growth of cells. If not treated at an initial phase it may lead to death. Despite many significant efforts and promising outcomes in this domain, accurate segmentation and classification remain a challenging task. We start reviewing the fundamental basics of the perception and neural networks with some fundamental theory that is often omitted. Doing to understand the reasons for the rise of deep learning in many application domainsfor these several medical imaging modalities and applications based on data mining method is proposed and developed. We provide a short overview of recent advances and some associated challenges in machine learning applied to medical image processing and image analysis. As this has become a very broad and fast expanding field we will not survey the entire landscape of applications, numerous research techniques have been introduced for BT detection as well as classification based on traditional machine learning (ML) and deep learning (DL). The traditional ML classifiers require hand‐crafted features. It is expected that soft set classifier will provide better results in terms of sensitivity specificity running time and overall classifier accuracy. Enhanced feature engineering aims to maximize the model's utility by converting raw data into numerical characteristics while preserving the original dataset's essence. This survey highlights the current clinical challenges, potential future solutions and opens up the researcher's challenges to evolve systematic brain tumor detection system demonstrating clinically acceptable better accuracy which will assist the radiologists in diagnosis.



Published

2024-06-30

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