Ensemble of deep capsule neural networks: an application to pediatric pneumonia prediction

Bodapati, Jyostna Devi; N, Rohith V; Dondeti, Venkatesulu

doi:10.1007/s13246-022-01169-5

Cited by 7 publications

(1 citation statement)

References 24 publications

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“…For instance, deep neural network models have achieved state-of-the-art results in tasks such as facial emotion recognition in the vision domain and sentiment analysis in the natural language domain [5,6]. These models have also been successful in disease prediction tasks such as diabetic retinopathy, pneumonia detection, and Covid-19 prediction in the healthcare domain [7][8][9]. Deep Neural networks are designed to function similarly to neurons in the human brain, allowing for reliable classification results with appropriate weights and layers.…”

Section: Introductionmentioning

confidence: 99%

A Deep Convolutional Neural Network Framework for Enhancing Brain Tumor Diagnosis on MRI Scans

Bodapati¹,

Ahmed²,

Chowdary³

et al. 2023

IDA

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Brain tumors are a critical public health concern, often resulting in limited life expectancy for patients. Accurate diagnosis of brain tumors is crucial to develop effective treatment strategies and improve patients' quality of life. Computer-aided diagnosis (CAD) systems that accurately classify tumor images have been challenging to develop. Deep convolutional neural network (DCNN) models have shown significant potential for tumor detection, and outperform traditional deep neural network models. In this study, a novel framework based on two pre-trained deep convolutional architectures (VGG16 and EfficientNetB0) is proposed for classifying different types of brain tumors, including meningioma, glioma, and pituitary tumors. Features are extracted from MR images using each architecture and merged before feeding them into machine learning algorithms for tumor classification. The proposed approach achieves a training accuracy of 98% and a test accuracy of 99% on the brain-tumorclassification-mri dataset available on Kaggle and btc_navoneel. The model shows promise to improve the accuracy and generalizability of medical image classification for better clinical decision support, ultimately leading to improved patient outcomes.

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