Výsledky vyhledávání - "Ghosh, Susmita"

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Introducing Feature Attention Module on Convolutional Neural Network for Diabetic Retinopathy Detection

Autor: Ghosh, Susmita, Chatterjee, Abhiroop

Diabetic retinopathy (DR) is a leading cause of blindness among diabetic patients. Deep learning models have shown promising results in automating the detection of DR. In the present work, we propose a new methodology that integrates a feature attent

Externí odkaz: http://arxiv.org/abs/2308.02985

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Transfer-Ensemble Learning based Deep Convolutional Neural Networks for Diabetic Retinopathy Classification

Autor: Ghosh, Susmita, Chatterjee, Abhiroop

This article aims to classify diabetic retinopathy (DR) disease into five different classes using an ensemble approach based on two popular pre-trained convolutional neural networks: VGG16 and Inception V3. The proposed model aims to leverage the str

Externí odkaz: http://arxiv.org/abs/2308.00525

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T-Fusion Net: A Novel Deep Neural Network Augmented with Multiple Localizations based Spatial Attention Mechanisms for Covid-19 Detection

Autor: Ghosh, Susmita, Chatterjee, Abhiroop

In recent years, deep neural networks are yielding better performance in image classification tasks. However, the increasing complexity of datasets and the demand for improved performance necessitate the exploration of innovative techniques. The pres

Externí odkaz: http://arxiv.org/abs/2308.00053

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Automated COVID-19 CT Image Classification using Multi-head Channel Attention in Deep CNN

Autor: Ghosh, Susmita, Chatterjee, Abhiroop

The rapid spread of COVID-19 has necessitated efficient and accurate diagnostic methods. Computed Tomography (CT) scan images have emerged as a valuable tool for detecting the disease. In this article, we present a novel deep learning approach for au

Externí odkaz: http://arxiv.org/abs/2308.00715

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Mutual Information Assisted Ensemble Recommender System for Identifying Critical Risk Factors in Healthcare Prognosis

Autor: Dey, Abhishek, Goswami, Debayan, Roy, Rahul, Ghosh, Susmita, Zhang, Yu Shrike, Chan, Jonathan H.

Purpose: Health recommenders act as important decision support systems, aiding patients and medical professionals in taking actions that lead to patients' well-being. These systems extract the information which may be of particular relevance to the e

Externí odkaz: http://arxiv.org/abs/2209.13836

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Autoencoder based Hybrid Multi-Task Predictor Network for Daily Open-High-Low-Close Prices Prediction of Indian Stocks

Autor: Chakraborty, Debasrita, Ghosh, Susmita, Ghosh, Ashish

Stock prices are highly volatile and sudden changes in trends are often very problematic for traditional forecasting models to handle. The standard Long Short Term Memory (LSTM) networks are regarded as the state-of-the-art models for such prediction

Externí odkaz: http://arxiv.org/abs/2204.13422

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