Frontiers in Medicine, Journal Year: 2024, Volume and Issue: 10
Published: Jan. 29, 2024
Introduction Oral Squamous Cell Carcinoma (OSCC) poses a significant challenge in oncology due to the absence of precise diagnostic tools, leading delays identifying condition. Current methods for OSCC have limitations accuracy and efficiency, highlighting need more reliable approaches. This study aims explore discriminative potential histopathological images oral epithelium OSCC. By utilizing database containing 1224 from 230 patients, captured at varying magnifications publicly available, customized deep learning model based on EfficientNetB3 was developed. The model’s objective differentiate between normal tissues by employing advanced techniques such as data augmentation, regularization, optimization. Methods research utilized imaging Cancer analysis, incorporating patients. These images, taken various magnifications, formed basis training specialized built upon architecture. underwent distinguish tissues, sophisticated methodologies including regularization techniques, optimization strategies. Results achieved success, showcasing remarkable 99% when tested dataset. high underscores efficacy effectively discerning tissues. Furthermore, exhibited impressive precision, recall, F1-score metrics, reinforcing its robust tool Discussion demonstrates promising models address challenges associated with ability achieve rate test dataset signifies considerable leap forward earlier accurate detection Leveraging machine learning, augmentation optimization, has shown results improving patient outcomes through timely identification
Language: Английский
Citations
34Harnessing artificial intelligence for predictive modelling in oral oncology: Opportunities, challenges, and clinical Perspectives
Oral Oncology Reports, Journal Year: 2024, Volume and Issue: 11, P. 100591 - 100591
Published: June 29, 2024
Artificial intelligence (AI) has emerged as a promising tool in oral oncology, particularly the field of prediction. This review provides comprehensive outlook on role AI predicting cancer, covering key aspects such data collection and preprocessing, machine learning techniques, performance evaluation validation, challenges, future prospects, implications for clinical practice. Various algorithms, including supervised learning, unsupervised deep approaches, have been discussed context cancer Additionally, challenges interpretability, accessibility, regulatory compliance, legal are addressed along with research directions potential impact oncology care.
Language: Английский
Citations
5Automated Skin Cancer Detection System Using Deep Transfer Learning
Published: Feb. 16, 2024
One of the most prevalent cancers, both melanoma and non-melanoma, causes hundreds thousands deaths globally each year. Skin cell growth that isn't normal is how it shows up. Recovery chances are significantly increased by early diagnosis. Furthermore, might reduce need for or use chemical, radiographic, surgical therapies altogether. A dermatoscope used in traditional method visual inspection a dermatologist primary care physician order to detect skin-related diseases. Patients who exhibit signs skin cancer referred biopsy histopathological examination confirm diagnosis determine appropriate course treatment. Recent developments deep convolutional neural networks (CNNs) have led automated classification with excellent performance accuracy comparable dermatologists. These advancements haven't, however, yet produced widely clinically reliable identification cancer. As result, medical expenses can be decreased. Dermoscopy, which examines general size, shape, color characteristics lesions, first step Suspected lesions then undergo additional sampling laboratory testing confirmation. Because learning artificial intelligence has become more popular, image-based advanced recent years.
Language: Английский
Citations
1Leveraging Convolutional Neural Networks for Classifying Lymphoma Using Histopathological Images
Published: June 28, 2024
Language: Английский
Citations
1Optimizing Deep Learning Based Approach for Brain Tumor Segmentation in Magnetic Resonance Imaging(MRI) Scans
Published: April 3, 2024
Language: Английский
Citations
0Customized Transfer Learning Models for Oral Squamous Cell Carcinoma Classification and Detection using Histopathological H&E Stained Images
SN Computer Science, Journal Year: 2024, Volume and Issue: 5(7)
Published: Sept. 11, 2024
Language: Английский
Citations
0An Efficient Approach towards Skin Cancer Diagnosis with EfficientNetB3
Journal of Innovative Image Processing, Journal Year: 2024, Volume and Issue: 6(4), P. 346 - 364
Published: Nov. 28, 2024
Skin cancer has been identified as the most widespread and well-documented type of malignancy worldwide. Its origin lies in irregular growth melanocytic cells, often referred to melanoma. Exposure ultraviolet radiation genetic factors leads melanoma appearing on skin. Identification at an early stage increases chances successful treatment. However, conventional biopsy method used for detecting skin is both invasive painful. It involves extensive laboratory procedures that consume a considerable amount time. Computer-aided diagnosis systems can help address these challenges. In this work, two distinct models developed based EfficientNetB3, with varying additional layers. To conduct comparative various cutting-edge techniques have evaluated. The suggested approaches surpass majority by achieving overall test accuracy 91% 93.25% Model 1 2 respectively.
Language: Английский
Citations
0Strengthening Oral Cancer Detection Using WDCNN
Published: Dec. 16, 2024
Language: Английский
Citations
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