[Retracted] Ant Colony Optimization‐Enabled CNN Deep Learning Technique for Accurate Detection of Cervical Cancer

BioMed Research International, Journal Year: 2023, Volume and Issue: 2023(1)

Published: Jan. 1, 2023

Cancer is characterized by abnormal cell growth and proliferation, which are both diagnostic indicators of the disease. When cancerous cells enter one organ, there a risk that they may spread to adjacent tissues eventually other organs. cervix uterus often initially manifests itself in uterine cervix, located at very bottom uterus. Both death cervical characteristic features this condition. False-negative results provide significant moral dilemma since cause women get an incorrect diagnosis cancer, turn can result woman's premature from False-positive do not raise any ethical concerns; but require patient go through expensive time-consuming treatment process, also experience tension anxiety warranted. In order detect cancer its earliest stages women, screening procedure known as Pap test performed. This article describes technique for improving images using Brightness Preserving Dynamic Fuzzy Histogram Equalization. To individual components find right area interest, fuzzy c-means approach applied. The segmented method interest. feature selection algorithm ACO algorithm. Following that, categorization carried out utilizing CNN, MLP, ANN algorithms.

Language: Английский

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Renyi Entropy Predictive Data Mining And Weighted Xavier Deep Neural Classifier For Heart Disease Prediction

International Journal of Computational and Experimental Science and Engineering, Journal Year: 2025, Volume and Issue: 11(1)

Published: Feb. 16, 2025

During the past few years, Frequent Pattern Mining (FPM) has received interest of several researchers that necessitate extracting items from transactions, and sequences datasets, clarifying heart disease diagnosis materializes commonly, recognizing specific arrangements. In this era with healthcare involving significant evolutions, unforeseeable movement enormous amount data concerning classification lead way to new issues in FPM, such as space time complexity. However, most research work concentrates on identifying patterns relating transpires frequently, where within every transaction were known a priori. To address present scenario, selecting predominant or frequent is essential using relevant FPM models. The primary objective enhance mining results reduce misclassification rate Cardiovascular Disease (CVD) dataset samples. This proposes novel method called Renyi Entropy Homogenized Weighted Xavier-based Deep Neural Classifier (REHWX-DNC) for prediction. tackle first challenge, Entropy-based (RE-FPM) algorithm proposed, which filters low-quality features function. handle second issue, HWX-DNC model designed assist minimizing by employing Swish activation A CVD synthesis can be analyzed obtain accuracy study, REGEX-DNC improved compared state-of-the-art methods. Some indicators, including prediction accuracy, time, level, F1-total, are considered calculate predictor, checking REHWX-DNC proposed efficient trustworthy predicting disease.

Language: Английский

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Comprehensive machine and deep learning analysis of sensor-based human activity recognition

Neural Computing and Applications, Journal Year: 2023, Volume and Issue: 35(17), P. 12793 - 12831

Published: March 8, 2023

Language: Английский

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An AI-based novel system for predicting respiratory support in COVID-19 patients through CT imaging analysis

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 8, 2024

Abstract The proposed AI-based diagnostic system aims to predict the respiratory support required for COVID-19 patients by analyzing correlation between lesions and level of provided patients. Computed tomography (CT) imaging will be used analyze three levels received patient: Level 0 (minimum support), 1 (non-invasive such as soft oxygen), 2 (invasive mechanical ventilation). begin segmenting from CT images creating an appearance model each lesion using a 2D, rotation-invariant, Markov–Gibbs random field (MGRF) model. Three MGRF-based models created, one support. This suggests that able differentiate different severity in decide patient neural network-based fusion system, which combines estimates Gibbs energy models. were assessed 307 COVID-19-infected patients, achieving accuracy $$97.72\%\pm 1.57$$ 97.72 % ± 1.57 , sensitivity $$97.76\%\pm 4.08$$ 97.76 4.08 specificity $$98.87\%\pm 2.09$$ 98.87 2.09 indicating high prediction accuracy.

Language: Английский

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Prostate cancer grading framework based on deep transfer learning and Aquila optimizer

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(14), P. 7877 - 7902

Published: Feb. 22, 2024

Abstract Prostate cancer is the one of most dominant among males. It represents leading death causes worldwide. Due to current evolution artificial intelligence in medical imaging, deep learning has been successfully applied diseases diagnosis. However, recent studies prostate classification suffers from either low accuracy or lack data. Therefore, present work introduces a hybrid framework for early and accurate segmentation using learning. The proposed consists two stages, namely stage stage. In stage, 8 pretrained convolutional neural networks were fine-tuned Aquila optimizer used classify patients normal ones. If patient diagnosed with cancer, segmenting cancerous spot overall image U-Net can help diagnosis, here comes importance trained on 3 different datasets order generalize framework. best reported accuracies are 88.91% MobileNet “ISUP Grade-wise Cancer” dataset 100% ResNet152 “Transverse Plane Dataset” precisions 89.22% 100%, respectively. model gives an average AUC 98.46% 0.9778, respectively, “PANDA: Resized Train Data (512 × 512)” dataset. results give indicator acceptable performance

Language: Английский

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A concentrated machine learning-based classification system for age-related macular degeneration (AMD) diagnosis using fundus images

Scientific Reports, Journal Year: 2024, Volume and Issue: 14(1)

Published: Jan. 29, 2024

Abstract The increase in eye disorders among older individuals has raised concerns, necessitating early detection through regular examinations. Age-related macular degeneration (AMD), a prevalent condition over 45, is leading cause of vision impairment the elderly. This paper presents comprehensive computer-aided diagnosis (CAD) framework to categorize fundus images into geographic atrophy (GA), intermediate AMD, normal, and wet AMD categories. crucial for precise age-related enabling timely intervention personalized treatment strategies. We have developed novel system that extracts both local global appearance markers from images. These are obtained entire retina iso-regions aligned with optical disc. Applying weighted majority voting on best classifiers improves performance, resulting an accuracy 96.85%, sensitivity 93.72%, specificity 97.89%, precision 93.86%, F1 ROC 95.85%, balanced 95.81%, sum 95.38%. not only achieves high but also provides detailed assessment severity each retinal region. approach ensures final aligns physician’s understanding aiding them ongoing follow-up patients.

Language: Английский

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Software Development Framework for Cardiac Disease Prediction Using Machine Learning Applications

Published: Dec. 23, 2022

The most difficult task in medicine is making a diagnosis of heart illness. Since the decision dependent on huge number clinical and pathological information, illness challenging. This such as resulted significant increase interest among academics medical professionals accurate efficient cardiac disease prediction. time essence cases sickness, getting appropriate quickly essential. leading cause death globally, early detection crucial. With proper case training testing, machine learning has recently emerged one advanced, trust worthy, helpful technologies industry, offering assistance for sickness main goal this endeavor to examine various prediction models choose pertinent variables using genetic approach. Genetically optimized outperform conventional terms performance. Analyzing UCI datasets. Cleveland database only that ML researchers have used thus far. patient's condition indicated "target" field. It positioned target column an integer value between 0 (no presence) 1 (presence). variable, while other factors are independent variables.

Language: Английский

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AutYOLO-ATT: an attention-based YOLOv8 algorithm for early autism diagnosis through facial expression recognition

Neural Computing and Applications, Journal Year: 2024, Volume and Issue: 36(27), P. 17199 - 17219

Published: June 6, 2024

Abstract Autism Spectrum Disorder (ASD) is a developmental condition resulting from abnormalities in brain structure and function, which can manifest as communication social interaction difficulties. Conventional methods for diagnosing ASD may not be effective the early stages of disorder. Hence, diagnosis crucial to improving patient's overall health well-being. One alternative method autism facial expression recognition since autistic children typically exhibit distinct expressions that aid distinguishing them other children. This paper provides deep convolutional neural network (DCNN)-based real-time emotion system kids. The proposed designed identify six emotions, including surprise, delight, sadness, fear, joy, natural, assist medical professionals families recognizing intervention. In this study, an attention-based YOLOv8 (AutYOLO-ATT) algorithm proposed, enhances model's performance by integrating attention mechanism. outperforms all classifiers metrics, achieving precision 93.97%, recall 97.5%, F1-score 92.99%, accuracy 97.2%. These results highlight potential real-world applications, particularly fields where high essential.

Language: Английский

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ECH3OA: An Enhanced Chimp-Harris Hawks Optimization Algorithm for copyright protection in Color Images using watermarking techniques

Knowledge-Based Systems, Journal Year: 2023, Volume and Issue: 269, P. 110494 - 110494

Published: March 27, 2023

Language: Английский

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A variate brain tumor segmentation, optimization, and recognition framework

Artificial Intelligence Review, Journal Year: 2022, Volume and Issue: 56(7), P. 7403 - 7456

Published: Dec. 15, 2022

Language: Английский

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