Enhancing Ovarian Cancer Detection: A Deep Learning Approach with MobileNetV3 and ResNet50 DOI

Chetna Vaid Kwatra,

Harpreet Kaur

Published: Nov. 22, 2023

Ovarian cancer, commonly known as the "silent killer," presents notable obstacles in terms of timely detection and management. This work aims to explore capabilities deep learning models, namely MobileNetV3 ResNet50, improving accuracy ovarian cancer. Using an extensive collection tissue photos, we performed a comparative examination two advanced convolutional neural networks (CNNs) assess their efficacy discriminating between cancerous non-cancerous samples.The findings our study indicate that ResNet50 exhibit considerable potential identification The model had rate 96.3%, highlighting its effectiveness early exhibited performance, with 92.08%. aforementioned results highlight models raising precision cancer detection, crucial measure patient outcomes. analysis presented this paper is significant resource for healthcare practitioners researchers, it provides insights into strengths limits these models. also lays groundwork future developments field gynecological malignancy diagnosis.

Language: Английский

Automated Ultrasonography of Hepatocellular Carcinoma using Discrete Wavelet Transform based Deep-learning Neural Network DOI Creative Commons
Se-Yeol Rhyou, Jae-Chern Yoo

Medical Image Analysis, Journal Year: 2025, Volume and Issue: 101, P. 103453 - 103453

Published: Jan. 5, 2025

Language: Английский

Citations

1
A novel metaheuristic-based approach for prediction of corrosion characteristics in offshore pipelines DOI

Mahdi shabani,

Michel Kadoch,

Seyedali Mirjalili

et al.

Engineering Failure Analysis, Journal Year: 2025, Volume and Issue: 170, P. 109231 - 109231

Published: Jan. 5, 2025

Language: Английский

Citations

0
Optimizing Breast Cancer Detection: Integrating Few‐Shot and Transfer Learning for Enhanced Accuracy and Efficiency DOI
Nadeem Sarwar,

Shaha Al‐Otaibi,

Asma Irshad

et al.

International Journal of Imaging Systems and Technology, Journal Year: 2025, Volume and Issue: 35(1)

Published: Jan. 1, 2025

ABSTRACT Breast cancer (BC) detection based on mammogram images is still an open issue, particularly when there little annotated data. Combining few‐shot learning (FSL) with transfer (TL) has been identified as a potential solution to overcome this problem due its ability learn from few examples while producing robust features for classification. The objective of study use and analyze FSL integrated TL enhance the classification accuracy generalization in limited dataset. proposed approach integrates models (prototypical networks, matching relation networks) procedures. are trained using small set samples annotation can be assessed various performance metrics. were compared state‐of‐the‐art methods regarding accuracy, precision, recall, F1‐score, area under ROC curve (AUC). proved effective integrated, networks model was most accurate, 95.6% AUC 0.970. provided higher F1‐scores, especially case discerning between normal, benign, malignant cases, traditional techniques recent techniques. This gives high efficiency, scalability whole BC process, it further medical imaging domains. Future research will explore hyperparameter tuning incorporating electronic health record systems diagnostic precision individualized care.

Language: Английский

Citations

0
Diagnosing tic disorders from videos using multi-phase learning DOI

XU Xiao-jing,

Ruizhe Zhang, Zi-Hao Bo

et al.

Computers & Electrical Engineering, Journal Year: 2025, Volume and Issue: 123, P. 110216 - 110216

Published: Feb. 26, 2025

Language: Английский

Citations

0
Deep Feature Fusion of Local and Global Patterns for Early Detection of Lung Abnormalities in Chest X-Rays DOI

Ashutosh Awasthi,

Pawan Kumar Tiwari, Dhirendra Verma

et al.

Communications in computer and information science, Journal Year: 2025, Volume and Issue: unknown, P. 363 - 377

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine Learning to Predict and Detect Bacterial Pneumonia Using Chest X-Rays DOI
Swarnambiga Ayyachamy,

Mridhula Manimaran,

Pradeep Kumar Yadalam

et al.

IGI Global eBooks, Journal Year: 2025, Volume and Issue: unknown, P. 151 - 172

Published: March 28, 2025

Pneumonia remains a critical global health issue, causing significant morbidity and mortality worldwide. This study addresses the urgent need for early accurate diagnosis by exploring potential of machine learning (ML) algorithms to enhance detection prediction pneumonia using chest X-ray images. The research highlights importance timely precise identification pneumonia-associated diagnostic findings. ML are employed automate process, reducing reliance on human interpretation while improving speed accuracy. A dataset 12,550 images, including cases with without lung abnormalities, is utilized train evaluate models. assesses Naive Bayes Logistic Regression algorithms, results indicating promising accuracy, achieving AUCs 0.725 0.689, respectively. Confusion matrices ROC curves further elucidate model performance. advances through ML, improved outcomes.

Language: Английский

Citations

0
Concrete section segmentation with advanced deep learning models and refined labeling approaches DOI
Woldeamanuel Minwuye Mesfin, Gun Kim, Hyeong-Ki Kim

et al.

Expert Systems with Applications, Journal Year: 2025, Volume and Issue: unknown, P. 127697 - 127697

Published: April 1, 2025

Language: Английский

Citations

0
A novel deep learning technique for multi classify Alzheimer disease: hyperparameter optimization technique DOI Creative Commons

A. S. Elmotelb,

Fayroz F. Sherif, A. S. Abohamama

et al.

Frontiers in Artificial Intelligence, Journal Year: 2025, Volume and Issue: 8

Published: April 24, 2025

A progressive brain disease that affects memory and cognitive function is Alzheimer’s (AD). To put therapies in place potentially slow the progression of AD, early diagnosis detection are essential. Early these phases enables activities, which essential for controlling disease. address issues with limited data computing resources, this work presents a novel deep-learning method based on using newly proposed hyperparameter optimization to identify hyperparameters ResNet152V2 model classifying AD more accurately. The compared state-of-the-art models divided into two categories: transfer learning classical showcase its effectiveness efficiency. This comparison four performance metrics: recall, precision, F1 score, accuracy. According experimental results, efficient effective various phases.

Language: Английский

Citations

0
Usefulness of Deep Learning-Based Reconstruction Techniques for Magnetic Resonance Imaging in Gamma Knife Radiosurgery Planning DOI
Chung-Hwan Kang

Journal of the Korean Society of MR Technology, Journal Year: 2025, Volume and Issue: 35(1), P. 9 - 18

Published: March 30, 2025

Language: Английский

Citations

0
Developing a classification system for brain tumors using the ResNet152V2 CNN model architecture DOI Creative Commons

Syahruu Siyammu Rhomadhon,

Diah Rahayu Ningtias

Journal of Soft Computing Exploration, Journal Year: 2024, Volume and Issue: 5(2), P. 173 - 182

Published: June 21, 2024

According to The American Cancer Society, in 2021 there were 24,530 cases of brain and nervous system tumors. National Institute reports that are approximately 4.4 new tumors per 100,000 men women year. Brain can be detected using magnetic resonance imaging (MRI), a scanning tool uses field computer record images is able provide clear visualization differences soft tissue such as white matter gray matter. However, this cannot done optimally because it still relies on manual analysis, so classify tumor types larger datasets with the potential for error low level accuracy. To accurately determine type tumor, better classification method needed. aim study accuracy calcification deep learning model. In study, was carried out ResNet152V2 convolutional neural network (CNN) model which has depth 152 layers. dataset used 7,023 MRI consisting 1,645 meningiomas, 1,621 gliomas, 1,757 pituitary 2,000 normal. Research results show an value 94.44%, concluded performs well classifying medium physicians more diagnose patients accurately.

Language: Английский

Citations

1