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All Journal Bulletin of Electrical Engineering and Informatics Indonesian Journal of Electrical Engineering and Computer Science
Rashmi, R.
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Electrical & Electronics Engineering

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DeepCervix: enhancing cervical cancer detection through transfer learning with VGG-16 architecture Joshi, Vaishali M.; Dandavate, Prajkta P.; Rashmi, R.; Shinde, Gitanjali R.; Thune, Neeta N.; Mirajkar, Riddhi
Indonesian Journal of Electrical Engineering and Computer Science Vol 36, No 3: December 2024
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v36.i3.pp1895-1903

Abstract

Cervical cancer remains a significant global health concern, emphasizing the urgent need for improved detection methods to ensure timely treatment. This research introduces a sophisticated methodology leveraging recent advances in medical imaging and deep learning algorithms to enhance the accuracy and efficiency of cervical cancer detection. The proposed approach comprises meticulous data preprocessing to ensure the integrity of input images, followed by the training of deep learning models including ResNet-50, AlexNet, and VGG-16, renowned for their performance in computer vision tasks. Evaluation metrics such as accuracy, precision, recall, and F1-score demonstrate the efficacy of the methodology, with an outstanding accuracy rate of 98% achieved. The model’s proficiency in accurately distinguishing healthy cervical tissue from cancerous tissue is underscored by precision, recall, and F1-score values. The primary strength of this deep learning-based approach lies in its potential for early detection, promising significant impact on cervical cancer diagnosis and treatment outcomes. This methodology contributes to advancements in medical imaging techniques, facilitating improved outcomes in cervical cancer detection and treatment.
DEMNET NeuroDeep: Alzheimer detection using electroencephalogram and deep learning M. Joshi, Vaishali; P. Dandavate, Prajkta; Rashmi, R.; R. Shinde, Gitanjali; D. Kulkarni, Deepthi; Mirajkar, Riddhi
Bulletin of Electrical Engineering and Informatics Vol 14, No 1: February 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/eei.v14i1.8163

Abstract

Alzheimer’s disease (AD) stands out as the most prevalent neurological brain disorder, and its diagnosis relies on various laboratory techniques. The electroencephalogram (EEG) emerges as a valuable tool for identifying AD, offering a quick, cost-effective, and readily accessible means of detecting early-stage dementia. Detecting AD in its early stages is crucial, as early intervention yields more successful outcomes and entails fewer risks than treating the disease at a later stage. The objective of this research is to create an advanced diagnosis system for AD using machine learning (ML) and EEG data. The proposed system utilizes a multilayer perceptron (MLP) and a deep neural network with bidirectional long short-term memory (BiLSTM) as the classifier. The feature extraction process involves incorporating Hjorth parameters, power spectral density (PSD), differential asymmetry (DASM), and differential entropy (DE). The BiLSTM classifier, particularly when combined with DE, exhibits outstanding performance with an accuracy of 97.27%. This amalgamation of DE and the deep neural network surpasses current state-of-the-art techniques, underscoring the substantial potential of this approach for precise and advanced diagnosis of AD.
Co-Authors D. Kulkarni, Deepthi Dandavate, Prajkta P. Joshi, Vaishali M. M. Joshi, Vaishali Mirajkar, Riddhi P. Dandavate, Prajkta R. Shinde, Gitanjali Shinde, Gitanjali R. Thune, Neeta N.