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Early Detection of Cancer

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A Robust End-to-End Deep Learning-Based Approach for Effective and Reliable BTD Using MR Images.

Sensors (Basel, Switzerland)
Detection of a brain tumor in the early stages is critical for clinical practice and survival rate. Brain tumors arise in multiple shapes, sizes, and features with various treatment options. Tumor detection manually is challenging, time-consuming, an...

Development of deep learning-assisted overscan decision algorithm in low-dose chest CT: Application to lung cancer screening in Korean National CT accreditation program.

PloS one
We propose a deep learning-assisted overscan decision algorithm in chest low-dose computed tomography (LDCT) applicable to the lung cancer screening. The algorithm reflects the radiologists' subjective evaluation criteria according to the Korea insti...

Diagnosis of Early Glottic Cancer Using Laryngeal Image and Voice Based on Ensemble Learning of Convolutional Neural Network Classifiers.

Journal of voice : official journal of the Voice Foundation
OBJECTIVES: The purpose of study is to improve the classification accuracy by comparing the results obtained by applying decision tree ensemble learning, which is one of the methods to increase the classification accuracy for a relatively small datas...

Cooperation between artificial intelligence and endoscopists for diagnosing invasion depth of early gastric cancer.

Gastric cancer : official journal of the International Gastric Cancer Association and the Japanese Gastric Cancer Association
BACKGROUND AND STUDY AIMS: The diagnostic ability of endoscopists to determine invasion depth of early gastric cancer is not favorable. We designed an artificial intelligence (AI) classifier for differentiating intramucosal and submucosal gastric can...

Field validation of deep learning based Point-of-Care device for early detection of oral malignant and potentially malignant disorders.

Scientific reports
Early detection of oral cancer in low-resource settings necessitates a Point-of-Care screening tool that empowers Frontline-Health-Workers (FHW). This study was conducted to validate the accuracy of Convolutional-Neural-Network (CNN) enabled m(mobile...

Towards Precision Oncology: Enhancing Cancer Screening, Diagnosis and Theragnosis Using Artificial Intelligence.

Current oncology (Toronto, Ont.)
Highly complex and multi-dimensional medical data containing clinical, radiologic, pathologic, and sociodemographic information have the potential to advance precision oncology [...].

Ovarian Cancer-Self Assessment: An Innovation for Early Detection and Risk Assessment of Ovarian Cancer.

Asian Pacific journal of cancer prevention : APJCP
OBJECTIVE: The modality to detect ovarian cancer at an early stage is very limited. Early diagnosis determines the prognosis. This study aimed to develop a risk assessment tool for early detection of ovarian cancer using artificial intelligence. To a...

Artificial intelligence for diagnosing gastric lesions under white-light endoscopy.

Surgical endoscopy
BACKGROUND: The ability of endoscopists to identify gastric lesions is uneven. Even experienced endoscopists may miss or misdiagnose lesions due to heavy workload or fatigue or subtle changes in lesions under white-light endoscopy (WLE). This study a...

HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening.

Scientific data
Histopathology is the gold standard method for staging and grading human tumors and provides critical information for the oncoteam's decision making. Highly-trained pathologists are needed for careful microscopic analysis of the slides produced from ...

Deep Learning and Structure-Based Virtual Screening for Drug Discovery against NEK7: A Novel Target for the Treatment of Cancer.

Molecules (Basel, Switzerland)
NIMA-related kinase7 (NEK7) plays a multifunctional role in cell division and NLRP3 inflammasone activation. A typical expression or any mutation in the genetic makeup of NEK7 leads to the development of cancer malignancies and fatal inflammatory dis...