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Accurate and efficient classification of different types of cancer is critical for early detection and effective treatment. In this paper, we present the results of our experiments using the EfficientNet algorithm for classification of…

图像与视频处理 · 电气工程与系统科学 2023-07-14 Romario Sameh Samir

Breast cancer is a leading cause of cancer-related deaths, but current programs are expensive and prone to false positives, leading to unnecessary follow-up and patient anxiety. This paper proposes a solution to automated breast cancer…

图像与视频处理 · 电气工程与系统科学 2023-07-24 Debajyoti Chakraborty

This research presents a machine-learning approach for tumor detection in medical images using convolutional neural networks (CNNs). The study focuses on preprocessing techniques to enhance image features relevant to tumor detection,…

图像与视频处理 · 电气工程与系统科学 2024-03-01 Ha Anh Vu

Cancer is one of the most life-threatening diseases worldwide, and head and neck (H&N) cancer is a prevalent type with hundreds of thousands of new cases recorded each year. Clinicians use medical imaging modalities such as computed…

图像与视频处理 · 电气工程与系统科学 2023-06-02 Ikboljon Sobirov

Colorectal cancer is the third most common tumor in men and the second in women, accounting for 10% of all tumors worldwide. It ranks second in cancer-related deaths with 9.4%, following lung cancer. The decrease in mortality rate…

其他定量生物学 · 定量生物学 2022-09-27 Panagiotis Katrakazas , Aristotelis Ballas , Marco Anisetti , Ilias Spais

Early diagnosis of renal cancer can greatly improve the survival rate of patients. Contrast-enhanced ultrasound (CEUS) is a cost-effective and non-invasive imaging technique and has become more and more frequently used for renal tumor…

图像与视频处理 · 电气工程与系统科学 2023-07-18 Junyu Li , Han Huang , Dong Ni , Wufeng Xue , Dongmei Zhu , Jun Cheng

Background: Breast ultrasound is prominently used in diagnosing breast tumors. At present, many automatic systems based on deep learning have been developed to help radiologists in diagnosis. However, training such systems remains…

计算机视觉与模式识别 · 计算机科学 2024-08-21 Yunxin Tang , Siyuan Tang , Jian Zhang , Hao Chen

Prognostication of medical problems using the clinical data by leveraging the Machine Learning techniques with stellar precision is one of the most important real world challenges at the present time. Considering the medical problem of…

机器学习 · 计算机科学 2022-08-16 Abhishek Gupta , Sannidhi Shetty , Raunak Joshi , Ronald Melwin Laban

Background: Pancreatic cancer is one of the most aggressive cancers, with poor survival rates. Endoscopic ultrasound (EUS) is a key diagnostic modality, but its effectiveness is constrained by operator subjectivity. This study evaluates a…

One of the deadliest cancers, lung cancer necessitates an early and precise diagnosis. Because patients have a better chance of recovering, early identification of lung cancer is crucial. This review looks at how to diagnose lung cancer…

机器学习 · 计算机科学 2025-01-22 Omid Shahriyar , Babak Nuri Moghaddam , Davoud Yousefi , Abbas Mirzaei , Farnaz Hoseini

Breast cancer is one of the leading causes of death among women across the globe. It is difficult to treat if detected at advanced stages, however, early detection can significantly increase chances of survival and improves lives of…

图像与视频处理 · 电气工程与系统科学 2023-08-17 Unaiza Sajid , Rizwan Ahmed Khan , Shahid Munir Shah , Sheeraz Arif

Breast cancer is the most common invasive cancer in women, and the second main cause of death. Breast cancer screening is an efficient method to detect indeterminate breast lesions early. The common approaches of screening for women are…

Skin cancer is the most common human malignancy(American Cancer Society) which is primarily diagnosed visually, starting with an initial clinical screening and followed potentially by dermoscopic(related to skin) analysis, a biopsy and…

图像与视频处理 · 电气工程与系统科学 2022-02-03 Kartikeya Agarwal , Tismeet Singh

Automated detection of cervical cancer cells or cell clumps has the potential to significantly reduce error rate and increase productivity in cervical cancer screening. However, most traditional methods rely on the success of accurate cell…

计算机视觉与模式识别 · 计算机科学 2019-12-24 Yixiong Liang , Zhihong Tang , Meng Yan , Jialin Chen , Qing Liu , Yao Xiang

Background: The 2022 update of the Ovarian-Adnexal Reporting and Data System (O-RADS) ultrasound classification refines risk stratification for adnexal lesions, yet human interpretation remains subject to variability and conservative…

Deep learning models benefit from training with a large dataset (labeled or unlabeled). Following this motivation, we present an approach to learn a deep learning model for the automatic segmentation of Organs at Risk (OARs) in cervical…

图像与视频处理 · 电气工程与系统科学 2023-02-22 Monika Grewal , Dustin van Weersel , Henrike Westerveld , Peter A. N. Bosman , Tanja Alderliesten

Prostate cancer is the second most common form of cancer, though most patients have a positive prognosis with many experiencing long-term survival with current treatment options. Yet, each treatment carries varying levels of intensity and…

定量方法 · 定量生物学 2024-10-31 Jefferson Zhou , Kahn Rhrissorrakrai

Lung cancer is the leading cause of cancer death and morbidity worldwide. Many studies have shown machine learning models to be effective at detecting lung nodules from chest X-ray images. However, these techniques have yet to be embraced…

图像与视频处理 · 电气工程与系统科学 2021-08-31 Michael J. Horry , Subrata Chakraborty , Biswajeet Pradhan , Manoranjan Paul , Douglas P. S. Gomes , Anwaar Ul-Haq

Purpose. High-grade serous ovarian carcinoma (HGSOC) is characterized by pronounced biological and spatial heterogeneity and is frequently diagnosed at an advanced stage. Neoadjuvant chemotherapy (NACT) followed by delayed primary surgery…

In the U.S, 5-10\% of new pediatric cases of cancer are primary bone tumors. The most common type of primary malignant bone tumor is osteosarcoma. The intention of the present work is to improve the detection and diagnosis of osteosarcoma…

图像与视频处理 · 电气工程与系统科学 2022-12-06 D M Anisuzzaman , Hosein Barzekar , Ling Tong , Jake Luo , Zeyun Yu