Acute Leukemia Classification and Prediction in Blood Cells Using Convolution Neural Network

Sundari, M. Shanmuga; Rani, M. Sudha; Ram, Kodumuri Bhargav

doi:10.1007/978-981-19-2821-5_11

Cited by 4 publications

(1 citation statement)

References 15 publications

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“…Intake tensor is slides through a procession of convolutional layers with group organization and ReLU activation, followed by a motion of depthwise differentiable complication layer with batch formalization and ReLU activation. Finally, the result of the convolutional layers [26] is acknowledged through a overall norm stacking level and a completely inter connected mesh to produce the final classification output.…”

Section: F Fully Connected Layermentioning

confidence: 99%

A Stable Method For Brain Tumor Prediction In Magnetic Resonance Images Using Fine-tuned XceptionNet

Sundari.M,

Divya,

Durga

et al. 2024

IJCDS

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Brain tumors can be a life-threatening condition, and early detection is crucial for effective treatment. Magnetic resonance imaging (MRI) is a valuable appliance for identifying the tumor's location, but manual detection is a time-engrossing and flaws-prone process. To overcome these challenges, computer-assisted approaches have been developed, and deep learning (DL) archetypes are now being pre-owned in medical imaging to discover brain tumors maneuver MRI carbon copies. In this, we propose a deep convolutional neural network (CNN) Xception net model for the efficient classification and detection of brain tumor images. We utilized the "Br35H :: Brain Tumor Detection 2020" dataset sourced from Kaggle, which encompasses 3000 MRI images of brain tumors, each with a file size of 88 megabytes. The Xception net is a powerful CNN model that has shown promising results in various systems perceiving exercise, in conjunction with medical illustration scrutiny. We fine-tuned the Xception net model using a dataset of Magnetic Resonance Imaging (MRI) images of the brain, which were pre-processed and labeled by medical experts. To reckon the performance of our prototype, we counselled dossier using a variety of interpretation criterion, including accuracy, precision, recall, and F1 score. Our customs view that the urged model achieved high accuracy in classifying brain tumor images. The archetype's strength to accurately and efficiently classify and detect brain tumors using MRI images can significantly improve patient outcomes by enabling early detection and treatment. Overall, our study demonstrates the persuasiveness of using the Xception net flawless for brain tumor ferreting out and alloting using MRI images with 94% of accuracy performance. The proposed model has the potential to revolutionize the department of salutary exemplify and improve patient outcomes for brain tumor treatment.

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Section: F Fully Connected Layermentioning

confidence: 99%