IEEE

A Novel CNN Architecture for Alzheimer's Disease Classification Using MRI Images: Comparison with Traditional Machine Learning Models

Abstract: Automated medical image processing has significantly improved with recent advances in deep learning and imaging technologies, particularly in the area of neuroimaging-based Alzheimer's ...
IEEE

Deep Learning Models for Precision Agriculture: Evaluating CNN Architectures for Accurate Plant Disease Detection

Abstract: Early and precise detection of plant diseases is crucial for enhancing crop yield and minimizing agricultural losses. This paper evaluates the performance of deep learning-based ...
IEEE

Comparison of CNN Model with Different Machine Learning Models for Fake News Detection

Abstract: Fake news continues to be a critical issue in today's era for any citizen concerned about political integrity and the state of governance. The use of internet has experienced exponential ...
IEEE

Epileptic Seizure Prediction Using a Deep Hybrid CNN-GAN Model on EEG Data

Abstract: Epileptic seizure detection from EEG signals is essential for the diagnosis and treatment of neurological conditions. However, this task is difficult because of the complex and fluctuating ...
IEEE

Deep Learning-Based Malaria Detection: A Robust CNN Framework for Accurate and Automated Diagnosis

Abstract: This research suggests a strong framework for automated malaria detection using a Convolutional Neural Network (CNN) model. The dataset, sourced from Kaggle, consists of 27,558 ...
IEEE

Deep Fake Detection using ResNext Convolutional Neural Network (CNN) combined with a Recurrent Neural Network (RNN)

Abstract: The utilization of digital video alterations has been observable for an extended period thanks to skillful deployment of visual enhancements; however, the latest progressions in deep ...
IEEE

Transfer Learning with Pre-Trained CNN Models: Advancing Colorectal Cancer Diagnosis

Abstract: Histopathological image analysis is vital for timely colorectal cancer diagnosis. Our study uses transfer learning techniques to create an efficient and robust classification method. Within ...