Cervical Cancer Diagnosis Using Intelligent Living Behavior of Artificial Jellyfish Optimized With Artificial Neural Network

Devarajan, Devikanniga; Alex, Doney; Mahesh, T R; Kumar, V. Vinoth; Aluvalu, Rajanikanth; Viswanadhula, Uma Maheswari; Shitharth, S.

doi:10.1109/access.2022.3221451

Cited by 36 publications

(14 citation statements)

References 27 publications

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“…However, for patients with persistent and repeated occult blood positive and no cause, great attention should be paid to colon cancer, especially right colon cancer. Carcinoembryonic Antibody (CEA) is a disease related to malignant tumors but has no specificity, and can become an auxiliary examination method [ 22 , 23 ].…”

Section: Factor Of Clinical Application In Diagnosis and Monitoring O...mentioning

confidence: 99%

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

Peng,

Deng

2024

BMC Med Imaging

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With the rapid development of medical imaging technology and computer technology, the medical imaging artificial intelligence of computer-aided diagnosis based on machine learning has become an important part of modern medical diagnosis. With the application of medical image security technology, people realize that the difficulty of its development is the inherent defect of advanced image processing technology. This paper introduces the background of colorectal cancer diagnosis and monitoring, and then carries out academic research on the medical imaging artificial intelligence of colorectal cancer diagnosis and monitoring and machine learning, and finally summarizes it with the advanced computational intelligence system for the application of safe medical imaging.In the experimental part, this paper wants to carry out the staging preparation stage. It was concluded that the staging preparation stage of group Y was higher than that of group X and the difference was statistically significant. Then the overall accuracy rate of multimodal medical image fusion was 69.5% through pathological staging comparison. Finally, the diagnostic rate, the number of patients with effective treatment and satisfaction were analyzed. Finally, the average diagnostic rate of the new diagnosis method was 8.75% higher than that of the traditional diagnosis method. With the development of computer science and technology, the application field was expanding constantly. Computer aided diagnosis technology combining computer and medical images has become a research hotspot.

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Section: Factor Of Clinical Application In Diagnosis and Monitoring O...mentioning

confidence: 99%

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

Peng,

Deng

2024

BMC Med Imaging

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“…Their bottom portion, which resembles an umbrella, aids in this movement by forcing the water outward in order to advance. The jellyfish bloom is another name for the jellyfish mass [25]. When favorable conditions, such as the availability of air, food, and temperature, are present, a swarm will be formed.…”

Section: Jellyfish Algorithmmentioning

confidence: 99%

An intelligent deep residual learning framework for tomato plant leaf disease classification

Ezhilarasan,

Rani Ranganathan,

Shri Mani

et al. 2024

IJECE

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Modern agriculture has been fascinated by various advancements in agriculture and the processing of foods and supply management that provision farmers to improve production. The health of plants is essential to improve production and economic growth. Diseases in plants can affect production and create a rigorous impact on the quality and create a hazard to food safety. Hence, detecting and classifying plant leaf diseases is essential to prevent the disease spread across the plants in the agriculture field and to improve productivity. The researchers in existing frameworks utilized artificial intelligence and machine learning techniques to demonstrate noteworthy solutions. However, a few issues exist related to noises in the images, hyperparameter selection problems, and over-fitting problems that influence prediction accuracy. The proposed model jellyfish ResNet(JF-ResNet) works well to achieve a better accuracy level by incorporating jellyfish optimized ResNET for tomato plant leaf disease identification and classification. The performance metrics such as Accuracy, specificity, sensitivity, and F1-Score is used to evaluate the performance of the JF-ResNet model. The proposed model achieves 97.3% accuracy, 95.3% sensitivity, 96.1% specificity, 96.9% recall, 96.4% precision and 97.1% F1-Score.

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“…Ref. [22] provides a detailed survey and different types of lung cancer with respect to the imaging. The survey further assures that the dependency is improved from one systems operation and dataset to an independent computing unit.…”

Section: Advanced Modelsmentioning

confidence: 99%

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

Subashchandrabose,

John,

Anbazhagu

et al. 2023

Diagnostics

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The early detection and classification of lung cancer is crucial for improving a patient’s outcome. However, the traditional classification methods are based on single machine learning models. Hence, this is limited by the availability and quality of data at the centralized computing server. In this paper, we propose an ensemble Federated Learning-based approach for multi-order lung cancer classification. This approach combines multiple machine learning models trained on different datasets allowing for improvising accuracy and generalization. Moreover, the Federated Learning approach enables the use of distributed data while ensuring data privacy and security. We evaluate the approach on a Kaggle cancer dataset and compare the results with traditional machine learning models. The results demonstrate an accuracy of 89.63% with lung cancer classification.

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Cervical Cancer Diagnosis Using Intelligent Living Behavior of Artificial Jellyfish Optimized With Artificial Neural Network

Cited by 36 publications

References 27 publications

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

Medical image fusion based on machine learning for health diagnosis and monitoring of colorectal cancer

An intelligent deep residual learning framework for tomato plant leaf disease classification

Ensemble Federated Learning Approach for Diagnostics of Multi-Order Lung Cancer

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