Development of Medical Image Retrieval and Classification using YOLOv7 Segmentation and Inception V3 Classifier DOI

K. Revathi,

S. Vijaya Kumar

2022 7th International Conference on Communication and Electronics Systems (ICCES), Год журнала: 2024, Номер unknown, С. 1169 - 1174

Опубликована: Дек. 16, 2024

Язык: Английский

Cancer diagnosis in smart healthcare: Optimization of the MamCancerX model’s multiple instance learning framework DOI

Yuliang Gai,

Ji Hao,

Yuxin Liu

и другие.

Alexandria Engineering Journal, Год журнала: 2025, Номер 125, С. 566 - 574

Опубликована: Апрель 23, 2025

Язык: Английский

Процитировано

0
Technological trends in 5G networks for IoT-enabled smart healthcare: A review DOI Creative Commons

Khandoker Hoque,

Md Boktiar Hossain,

Anhar Sami

и другие.

International Journal of Science and Research Archive, Год журнала: 2024, Номер 12(2), С. 1399 - 1410

Опубликована: Июль 30, 2024

Smart healthcare is in the process of quick evolution from traditional focused approach towards specialist and hospital to a patient-centric model. The following technological advancements have boosted this revolution vertical. Presently, 4G as well other communication standards like WLAN are applied offer smart services solutions. considers apply for advancement further future. It reason that industry expands, several applications anticipated generate huge volume data various forms sizes. Thus, enormous varying requires special end-to-end delay, bandwidth, latency factors. it becomes highly challenging current technologies effectively support complex sensitive health care these 5G networks being planned implemented address multifaceted requirements IoT. assisted consist IoT devices which need better network performance extended cellular connections. There issues with existing connectivity solutions namely how many can be connected, achieving global standardization, optimizing low power budgets, fit into given area secure communication. This paper aims provide an elaborate review by technology.

Язык: Английский

Процитировано

3
Enhancing Dermatological Diagnostics with EfficientNet: A Deep Learning Approach DOI Creative Commons
Ionela Manole, Alexandra‐Irina Butacu,

Raluca Nicoleta Bejan

и другие.

Bioengineering, Год журнала: 2024, Номер 11(8), С. 810 - 810

Опубликована: Авг. 9, 2024

: Despite recent advancements, medical technology has not yet reached its peak. Precision medicine is growing rapidly, thanks to machine learning breakthroughs powered by increased computational capabilities. This article explores a deep application for computer-aided diagnosis in dermatology.

Язык: Английский

Процитировано

2
Total Productivity Optimization (TPO): A Case Study in Plastic Manufacturing Industry DOI

Joyeshree Biswas,

Suman G. Das

SSRN Electronic Journal, Год журнала: 2024, Номер unknown

Опубликована: Янв. 1, 2024

Язык: Английский

Процитировано

0
Optimizing Healthcare Operations With AI Algorithms by Enhancing Skin Cancer Diagnosis Using Advanced Image Processing and Classification Techniques DOI

Abioye Abiodun Oluwasegun,

Abraham E. Evwiekpaefe,

Philip O. Odion

и другие.

Advances in healthcare information systems and administration book series, Год журнала: 2024, Номер unknown, С. 235 - 276

Опубликована: Ноя. 27, 2024

Optimizing healthcare through AI algorithms offers significant potential in skin cancer diagnosis. Skin cancer, involving abnormal cell growth, includes melanoma, the most dangerous form. Early detection is crucial, but traditional methods like visual inspection and biopsy are time-consuming subjective. provides a more efficient, objective approach. This chapter enhances diagnostic accuracy using advanced image processing classification on comprehensive dataset with seven classes. Initially imbalanced, data augmentation balanced it, generating 2000 images per class. Gray Level Co-occurrence Matrix (GLCM) Color Histogram were used for feature extraction, combined Random Forest classifier. The best model achieved 97% accuracy, emphasizing effective extraction AI-based

Язык: Английский

Процитировано

0
Enhanced Classification of Skin Lesions Using Fine-Tuned MobileNet and DenseNet121 Models with Ensemble Learning DOI Open Access
Yasin Sancar

Erzincan Üniversitesi Fen Bilimleri Enstitüsü Dergisi, Год журнала: 2024, Номер 17(3), С. 870 - 883

Опубликована: Дек. 27, 2024

This study presents a deep learning approach for early detection of melanoma, one the most dangerous skin cancers. In this article, all pre-trained models Keras library are trained with ISIC cancer dataset available on Kaggle and accuracy each model is analyzed in detail. With results obtained from models, were fine-tuned to further optimize performance model. After re-evaluation fine-tuning, rates compared: DenseNet121 MobileNet found be two best high among models. As such, these combined an ensemble achieve better overall accuracy. The rate 93.03%. Therefore, learning-based method appears reliable powerful technique that can used diagnose serious diseases such as cancer. provide support system great potential assist dermatologists phase by easing workload improving patient outcomes.

Язык: Английский

Процитировано

0
Development of Medical Image Retrieval and Classification using YOLOv7 Segmentation and Inception V3 Classifier DOI

K. Revathi,

S. Vijaya Kumar

2022 7th International Conference on Communication and Electronics Systems (ICCES), Год журнала: 2024, Номер unknown, С. 1169 - 1174

Опубликована: Дек. 16, 2024

Язык: Английский

Процитировано

0