A novel Parametric Flatten-p Mish activation function based deep CNN model for brain tumor classification

Computers in Biology and Medicine, Journal Year: 2022, Volume and Issue: 150, P. 106183 - 106183

Published: Oct. 14, 2022

Language: Английский

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Optimization System Based on Convolutional Neural Network and Internet of Medical Things for Early Diagnosis of Lung Cancer

Bioengineering, Journal Year: 2023, Volume and Issue: 10(3), P. 320 - 320

Published: March 2, 2023

Recently, deep learning and the Internet of Things (IoT) have been widely used in healthcare monitoring system for decision making. Disease prediction is one emerging applications current practices. In method described this paper, lung cancer implemented using IoT, which a challenging task computer-aided diagnosis (CAD). Because dangerous medical disease that must be identified at higher detection rate, disease-related information obtained from IoT devices transmitted to server. The data are then processed classified into two categories, benign malignant, multi-layer CNN (ML-CNN) model. addition, particle swarm optimization improve ability (loss accuracy). This step uses (CT scan sensor information) based on Medical (IoMT). For purpose, image IoMT sensors gathered, classification actions taken. performance proposed technique compared with well-known existing methods, such as Support Vector Machine (SVM), probabilistic neural network (PNN), conventional CNN, terms accuracy, precision, sensitivity, specificity, F-score, computation time. datasets were tested evaluate performance: Lung Image Database Consortium (LIDC) Linear Imaging Self-Scanning Sensor (LISS) datasets. Compared alternative trial outcomes showed suggested has potential help radiologist make an accurate efficient early diagnosis. ML-CNN was analyzed Python, where accuracy (2.5-10.5%) high when number instances, precision (2.3-9.5%) sensitivity (2.4-12.5%) several F-score (2-30%) cases, error rate (0.7-11.5%) low time (170 ms 400 ms) how many cases computed work, including previous known methods. architecture shows outperforms works.

Language: Английский

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Healthcare As a Service (HAAS): CNN-based cloud computing model for ubiquitous access to lung cancer diagnosis

Heliyon, Journal Year: 2023, Volume and Issue: 9(11), P. e21520 - e21520

Published: Oct. 27, 2023

The field of automated lung cancer diagnosis using Computed Tomography (CT) scans has been significantly advanced by the precise predictions offered Convolutional Neural Network (CNN)-based classifiers. Critical areas study include improving image quality, optimizing learning algorithms, and enhancing diagnostic accuracy. To facilitate a seamless transition from research laboratories to real-world applications, it is crucial improve technology's usability-a factor often neglected in current state-of-the-art research. Yet, this frequently overlooks need for expediting process. This paper introduces Healthcare-As-A-Service (HAAS), an innovative concept inspired Software-As-A-Service (SAAS) within cloud computing paradigm. As comprehensive service system, HAAS potential reduce mortality rates providing early opportunities everyone. We present HAASNet, cloud-compatible CNN that boasts accuracy rate 96.07%. By integrating HAASNet with physio-symptomatic data Internet Medical Things (IoMT), proposed model generates accurate reliable reports. Leveraging IoMT technology, globally accessible via Internet, transcending geographic boundaries. groundbreaking achieves average precision, recall, F1-scores 96.47%, 95.39%, 94.81%, respectively.

Language: Английский

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A Novel Front Door Security (FDS) Algorithm Using GoogleNet-BiLSTM Hybridization

IEEE Access, Journal Year: 2023, Volume and Issue: 11, P. 19122 - 19134

Published: Jan. 1, 2023

Security has always been a significant concern since the dawn of human civilization. That is why we build houses to keep ourselves and our belongings safe. And do not hesitate spend lot on front-door locks install CCTV cameras monitor security threats. This paper presents an innovative automatic Front Door (FDS) algorithm that uses Human Activity Recognition (HAR) detect four different threats at front door from real-time video feed with 73.18% accuracy. The activities are recognized using combination GoogleNet-BiLSTM hybrid network. network receives camera classifies activities. proposed this classification alert any attempts break by kicking, punching, or hitting. Furthermore, FDS effective in detecting gun violence door, which further strengthens security. (HAR)-based novel demonstrates potential ensuring better safety 71.49% precision, 68.2% recall, F1-score 0.65.

Language: Английский

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Bacterial Strain Classification using Convolutional Neural Network for Automatic Bacterial Disease Diagnosis

2022 12th International Conference on Cloud Computing, Data Science & Engineering (Confluence), Journal Year: 2023, Volume and Issue: unknown, P. 325 - 332

Published: Jan. 19, 2023

Diseases caused by bacterial contamination are common causes of human illness. Different strains responsible for different types diseases. There more than 4,900 so far have been discovered. That is why it impractical to start the treatment diseases attacks without diagnosing particular strain that The traditional method classification from specimens still widely used in microbiological practice clinical application. However, s a time-consuming process and requires well-trained, experienced microbiologists. This paper proposes computer-aided artificial intelligent-based automatic faster methods potentially better alternative. We designed, optimized, experimented with Convolutional Neural Network (CNN) automatically classify digital images captured using an SC30 camera Olympus CX31 Upright Biological Microscope. proposed network classifies 95.12% accuracy, 96.01% precision, 96.70% recall, 4.88% error rate. uses innovative image augmentation overcome limitation number training images. performs similar approaches regarding accuracy simplicity.

Language: Английский

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Lung Sound Classification With Multi-Feature Integration Utilizing Lightweight CNN Model

IEEE Access, Journal Year: 2024, Volume and Issue: 12, P. 21262 - 21276

Published: Jan. 1, 2024

Detecting respiratory diseases is of utmost importance, considering that ailments represent one the most prevalent categories globally. The initial stage lung disease detection involves auscultation conducted by specialists, relying significantly on their expertise. Therefore, automating process for can yield enhanced efficiency. Artificial intelligence (AI) has shown promise in improving accuracy sound classification extracting features from sounds are relevant to task and learning relationships between these different pulmonary diseases. This paper utilizes two publicly available recordings namely, ICBHI 2017 challenge dataset another at Mendeley Data. Foremost this paper, we provide a detailed exposition about employing Convolutional Neural Network (CNN) feature extraction Mel spectrograms, frequency cepstral coefficients (MFCCs), Chromagram. highest achieved developed 91.04% 10 classes. Extending contribution, elaborates explanation model prediction Explainable Intelligence (XAI). novel contribution study CNN classifies into classes combining audio-specific enhance process.

Language: Английский

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Knowledge distillation model for Acute Lymphoblastic Leukemia Detection: Exploring the impact of nesterov-accelerated adaptive moment estimation optimizer

Biomedical Signal Processing and Control, Journal Year: 2024, Volume and Issue: 94, P. 106246 - 106246

Published: March 30, 2024

Language: Английский

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MENet: A Mitscherlich function based ensemble of CNN models to classify lung cancer using CT scans

PLoS ONE, Journal Year: 2024, Volume and Issue: 19(3), P. e0298527 - e0298527

Published: March 11, 2024

Lung cancer is one of the leading causes cancer-related deaths worldwide. To reduce mortality rate, early detection and proper treatment should be ensured. Computer-aided diagnosis methods analyze different modalities medical images to increase diagnostic precision. In this paper, we propose an ensemble model, called Mitscherlich function-based Ensemble Network (MENet), which combines prediction probabilities obtained from three deep learning models, namely Xception, InceptionResNetV2, MobileNetV2, improve accuracy a lung model. The approach based on function, produces fuzzy rank combine outputs said base classifiers. proposed method trained tested two publicly available datasets, Iraq-Oncology Teaching Hospital/National Center for Cancer Diseases (IQ-OTH/NCCD) LIDC-IDRI, both these are computed tomography (CT) scan datasets. results in terms some standard metrics show that performs better than state-of-the-art methods. codes work at https://github.com/SuryaMajumder/MENet .

Language: Английский

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AI-powered COVID-19 forecasting: a comprehensive comparison of advanced deep learning methods

Osong Public Health and Research Perspectives, Journal Year: 2024, Volume and Issue: 15(2), P. 115 - 136

Published: March 28, 2024

Objectives: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges the public health sector, including that of United Arab Emirates (UAE). objective this study was assess efficiency and accuracy various deep-learning models in forecasting COVID-19 cases within UAE, thereby aiding nation’s authorities informed decision-making.Methods: This utilized a comprehensive dataset encompassing confirmed cases, demographic statistics, socioeconomic indicators. Several advanced deep learning models, long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, recurrent (RNN) were trained evaluated. Bayesian optimization also implemented fine-tune these models.Results: evaluation framework revealed each model exhibited different levels predictive precision. Specifically, RNN outperformed other architectures even without optimization. Comprehensive perspective analytics conducted scrutinize dataset.Conclusion: transcends academic boundaries by offering critical insights enable UAE deploy targeted data-driven interventions. model, which identified as most reliable accurate for specific context, can significantly influence decisions. Moreover, broader implications research validate capability techniques handling complex datasets, thus transformative potential healthcare sectors.

Language: Английский

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Design and development of artificial intelligence‐based application programming interface for early detection and diagnosis of colorectal cancer from wireless capsule endoscopy images

International Journal of Imaging Systems and Technology, Journal Year: 2024, Volume and Issue: 34(2)

Published: Feb. 5, 2024

Abstract The colorectal cancer (CRC) is gaining attention in the context of gastrointestinal tract diseases as it ranks third among most prevalent type cancer. early diagnosis CRC can be done by periodic examination colon and rectum for innocuous tissue abnormality called polyp has potential to evolve malignant future. using wireless capsule endoscopy requires dedicated commitment medical expert demanding significant time, focus effort. accuracy manual analysis identifying polyps extensively reliant on cognitive condition physician, thus emphasizing requirement automatic identification. artificial intelligence integrated computer‐aided system could assist clinician better diagnosis, thereby reducing miss‐rates polyps. In our proposed study, we developed an application program interface aid segmentation evaluation its dimension placement four landmarks predicted polyp. performed light weight Padded U‐Net effective images. We trained validated with augmented images Kvasir dataset calculated performance parameters. order facilitate image augmentation, a graphical user Augment Tree was developed, which incorporates 92 augmentation techniques. accuracy, recall, precision, IoU, F1‐score, loss achieved during validation were 95.6%, 0.946%, 0.985%, 0.933%, 0.965% 0.080% respectively. demonstrated that improved reduced when model rather than only limited original On comparison U‐net architecture recently architectures, attained optimal all metrics except marginal highest value.

Language: Английский

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