scholarly journals Early-Stage Detection of Cancer in Breast Using Artificial Intelligence

2021 ◽  
Vol 11 (2) ◽  
pp. 2016-2028
Author(s):  
M.N. Vimal Kumar ◽  
S. Aakash Ram ◽  
C. Shobana Nageswari ◽  
C. Raveena ◽  
S. Rajan
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Deep Learning Algorithm ◽  
Common Cancer ◽  
Abnormal Cells ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

One of the deadly diseases among humans is Cancer, which occurs almost anywhere in the human body. Cancer is caused by the cells that spread into the surrounding tissues by dividing itself uncontrollably. Breast Cancer is the most common cancer among women. Early detection and diagnosis of breast cancer are treatable and curable. Many women have no symptoms for this cancer at an early stage. The abnormal cells in the breast will risk for the development of breast cancer. So, it is important for women to regularly examine their breast. Technologies can be utilized in a smarter way with Artificial Intelligence techniques to assist the women during their examination of the breast at their living place to avoid the risk of breast cancer. The main aim is to develop a lowcost self-examining device for the detection of breast cancer and abnormality in the breast using an efficient optical method, Deep-learning algorithm and Internet of Things.

scholarly journals Rib fracture detection system based on deep learning

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liding Yao ◽  
Xiaojun Guan ◽  
Xiaowei Song ◽  
Yanbin Tan ◽  
Chun Wang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Detection System ◽  
Rib Fracture ◽  
Diagnostic Efficiency ◽  
Fracture Detection ◽  
Deep Learning Algorithm ◽  
Experienced Radiologist ◽  
Comparable Performance ◽  
Detection And Diagnosis

AbstractRib fracture detection is time-consuming and demanding work for radiologists. This study aimed to introduce a novel rib fracture detection system based on deep learning which can help radiologists to diagnose rib fractures in chest computer tomography (CT) images conveniently and accurately. A total of 1707 patients were included in this study from a single center. We developed a novel rib fracture detection system on chest CT using a three-step algorithm. According to the examination time, 1507, 100 and 100 patients were allocated to the training set, the validation set and the testing set, respectively. Free Response ROC analysis was performed to evaluate the sensitivity and false positivity of the deep learning algorithm. Precision, recall, F1-score, negative predictive value (NPV) and detection and diagnosis were selected as evaluation metrics to compare the diagnostic efficiency of this system with radiologists. The radiologist-only study was used as a benchmark and the radiologist-model collaboration study was evaluated to assess the model’s clinical applicability. A total of 50,170,399 blocks (fracture blocks, 91,574; normal blocks, 50,078,825) were labelled for training. The F1-score of the Rib Fracture Detection System was 0.890 and the precision, recall and NPV values were 0.869, 0.913 and 0.969, respectively. By interacting with this detection system, the F1-score of the junior and the experienced radiologists had improved from 0.796 to 0.925 and 0.889 to 0.970, respectively; the recall scores had increased from 0.693 to 0.920 and 0.853 to 0.972, respectively. On average, the diagnosis time of radiologist assisted with this detection system was reduced by 65.3 s. The constructed Rib Fracture Detection System has a comparable performance with the experienced radiologist and is readily available to automatically detect rib fracture in the clinical setting with high efficacy, which could reduce diagnosis time and radiologists’ workload in the clinical practice.

scholarly journals Annotating and Detecting Topics in Social Media Forum and Modelling the Annotation to Derive Directions-A Case Study

2021 ◽  
Author(s):  
Athira B ◽  
Josette Jones ◽  
Sumam Mary Idicula ◽  
Anand Kulanthaivel ◽  
Enming Zhang
Keyword(s):  
Breast Cancer ◽  
Social Media ◽  
Deep Learning ◽  
Cancer Patients ◽  
Learning Algorithm ◽  
Treatment Options ◽  
Healthcare Sector ◽  
Final Decision ◽  
Breast Cancer Patients ◽  
Deep Learning Algorithm

Abstract The widespread influence of social media impacts every aspect of life, including the healthcare sector. Although medics and health professionals are the final decision makers, the advice and recommendations obtained from fellow patients are significant. In this context, the present paper explores the topics of discussion posted by breast cancer patients and survivors on online forums. The study examines an online forum, Breastcancer.org, maps the discussion entries to several topics, and proposes a machine learning model based on a classification algorithm to characterize the topics. To explore the topics of breast cancer patients and survivors, approximately 1000 posts are selected and manually labeled with annotations. In contrast, millions of posts are available to build the labels. A semi-supervised learning technique is used to build the labels for the unlabeled data; hence, the large data are classified using a deep learning algorithm. The deep learning algorithm BiLSTM with BERT word embedding technique provided a better f1-score of 79.5%. This method is able to classify the following topics: medication reviews, clinician knowledge, various treatment options, seeking and providing support, diagnostic procedures, financial issues and implications for everyday life. What matters the most for the patients is coping with everyday living as well as seeking and providing emotional and informational support. The approach and findings show the potential of studying social media to provide insight into patients' experiences with cancer like critical health problems.

scholarly journals AN IN-DEPTH ANALYSIS OF THE IDENTIFIED ALGORITHMS AND THEIR COMPARATIVE STUDY IN THE EARLY DETECTION AND DIAGNOSIS OF BREAST CANCER

2021 ◽  
Vol 12 (1) ◽  
Author(s):  
Mridul Sharma
Keyword(s):  
Breast Cancer ◽  
Artificial Intelligence ◽  
Early Detection ◽  
Healthcare Workers ◽  
State Of The Art ◽  
Machine Learning Algorithms ◽  
Support Vector ◽  
Depth Analysis ◽  
Detection And Diagnosis ◽  
Early Detection And Diagnosis

These days one of the major inevitable ailments for females is bosom malignancy. The appropriate medication and early findings are important stages to take to thwart this ailment. Although, it's not easy to recognize due to its few vulnerabilities and lack of data. Can use artificial intelligence to create devices that can help doctors and healthcare workers to early detection of this cancer. In This research, we investigate three specific machine learning algorithms widely used to detect bosom ailments in the breast region. These algorithms are Support vector machine (SVM), Bayesian Networks (BN) and Random Forest (RF). The output in this research is based on the State-of-the-art technique.

scholarly journals A Novel System for the Identification of Diabetic Retinopathy using Computer Based Statistical Classification

2021 ◽  
Vol 12 (3) ◽  
pp. 4044-4059
Author(s):  
Sunil S S Et.al
Keyword(s):  
Deep Learning ◽  
Learning Strategy ◽  
Early Stage ◽  
Diabetic Patients ◽  
Statistical Classification ◽  
Computer Based ◽  
Detection And Diagnosis ◽  
Different Characteristics ◽  
Early Detection And Diagnosis

Diabetes Retinopathy (DR) is an eye disorder that affects the human retina due to increased insulin levels in the blood. Early detection and diagnosis of DR is essential in the optimal treatment of diabetic patients. The current research is to develop controls for identifying different characteristics and differences in colour  retina and using different classifications. This therapeutic approach describes diabetes recovery from data collected from multiple fields including DRIDB0, DRIDB1, MESSIDOR, STARE and HRF. Here  machine learning, neural networks and deep learning algorithms issues are addressed with related topics such as Sensitivity, Precision, Accuracy, Error,   Specificity and F1-score, Mathews Correlation Coefficient (MCC) and coefficient of kappa are compared. Finally due to the deep learning strategy the results were more effective compared to other methods. The system can help ophthalmologists, to identify the symptoms of diabetes at an early stage, for better treatment and to improve the quality of life biology.

scholarly journals Deep Learning for Automated Detection of Cyst and Tumors of the Jaw in Panoramic Radiographs

2020 ◽  
Vol 9 (6) ◽  
pp. 1839
Author(s):  
Hyunwoo Yang ◽  
Eun Jo ◽  
Hyung Jun Kim ◽  
In-ho Cha ◽  
Young-Soo Jung ◽  
...  
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Early Stage ◽  
Maxillofacial Surgery ◽  
Odontogenic Cysts ◽  
Oral And Maxillofacial Surgery ◽  
Panoramic Radiographs ◽  
Convolutional Networks ◽  
Deep Learning Algorithm ◽  
Dentigerous Cysts

Patients with odontogenic cysts and tumors may have to undergo serious surgery unless the lesion is properly detected at the early stage. The purpose of this study is to evaluate the diagnostic performance of the real-time object detecting deep convolutional neural network You Only Look Once (YOLO) v2—a deep learning algorithm that can both detect and classify an object at the same time—on panoramic radiographs. In this study, 1602 lesions on panoramic radiographs taken from 2010 to 2019 at Yonsei University Dental Hospital were selected as a database. Images were classified and labeled into four categories: dentigerous cysts, odontogenic keratocyst, ameloblastoma, and no lesion. Comparative analysis among three groups (YOLO, oral and maxillofacial surgeons, and general practitioners) was done in terms of precision, recall, accuracy, and F1 score. While YOLO ranked highest among the three groups (precision = 0.707, recall = 0.680), the performance differences between the machine and clinicians were statistically insignificant. The results of this study indicate the usefulness of auto-detecting convolutional networks in certain pathology detection and thus morbidity prevention in the field of oral and maxillofacial surgery.

Sign in / Sign up

Export Citation Format

Share Document