Computer-aided diagnostic system for breast cancer by detecting microcalcifications

1998 ◽  
Author(s):  
Chul S. Lee ◽  
Jong K. Kim ◽  
Hyun Wook Park
Keyword(s):  
Breast Cancer ◽  
Diagnostic System ◽  
Computer Aided

scholarly journals Computer aided diagnosis for breast cancer based on the gabor filter technique

2020 ◽  
Vol 10 (5) ◽  
pp. 5235 ◽  
Author(s):  
Mohammed Y. Kamil
Keyword(s):  
Breast Cancer ◽  
Gabor Filter ◽  
Diagnostic System ◽  
Region Of Interest ◽  
Histogram Equalization ◽  
Support Vector ◽  
Cancer Early Detection ◽  
False Positive Reduction ◽  
Computer Aided ◽  
Aided Diagnosis

The most prominent reason for the death of women all over the world is breast cancer. Early detection of cancer helps to lower the death rate. Mammography scans determine breast tumors in the first stage. As the mammograms have slight contrast, thus, it is a blur to the radiologist to recognize micro growths. A computer-aided diagnostic system is a powerful tool for understanding mammograms. Also, the specialist helps determine the presence of the breast lesion and distinguish between the normal area and the mass. In this paper, the Gabor filter is presented as a key step in building a diagnostic system. It is considered a sufficient method to extract the features. That helps us to avoid tumor classification difficulties and false-positive reduction. The linear support vector machine technique is used in this system for results classification. To improve the results, adaptive histogram equalization pre-processing procedure is employed. Mini-MIAS database utilized to evaluate this method. The highest accuracy, sensitivity, and specificity achieved are 98.7%, 98%, 99%, respectively, at the region of interest (30×30). The results have demonstrated the efficacy and accuracy of the proposed method of helping the radiologist on diagnosing breast cancer.

scholarly journals BI-RADS Breast Tumor Classification Through Image Mining

2019 ◽  
Author(s):  
Valesca J. S. Da Silva ◽  
Mateus M. R. Da Silva ◽  
Marcelino P. S. Silva ◽  
Joana R. C. Nogueira
Keyword(s):  
Breast Cancer ◽  
Image Processing ◽  
Breast Tumor ◽  
Diagnostic System ◽  
Tumor Classification ◽  
Image Mining ◽  
Computer Aided ◽  
Breast Tumor Classification

In this article, a computer aided diagnostic system for BI-RADS classification of breast cancer is proposed. The approach involves image processing capabilities to extract features from tumors in mammography and image mining to classify them as BI-RADS 2, BI-RADS 3, BI-RADS 4C or BI-RADS 5. Images from the BCDR repository were used for the experiments. The results showed the efficacy of the proposed method, which classified tumors with considerable accuracy in four BI-RADS categories.

Observer Variation in Clinical Diagnosis - A Computer-Aided Assessment of its Magnitude and Importance in 552 Patients with Abdominal Pain

1973 ◽  
Vol 12 (02) ◽  
pp. 108-113 ◽  
Author(s):  
P. W. Gill ◽  
D. J. Leaper ◽  
P. J. Guillou ◽  
J. R. Staniland ◽  
J. C. Horhocks ◽  
...  
Keyword(s):  
Abdominal Pain ◽  
Clinical Diagnosis ◽  
Error Rate ◽  
Diagnostic System ◽  
Diagnostic Error ◽  
Observer Variation ◽  
History Taking ◽  
Computer Aided ◽  
Computer Based ◽  
Initial Survey

This report describes an evaluation of »observer variation« in history taking and examination of patients with abdominal pain. After an initial survey in which the degree of observer variation amongst the present authors fully confirmed previous rather gloomy forecasts, a system of »agreed definitions« was produced, and further studies showed a rapid and considerable fall in the degree of observer variation between the data recorded by the same authors. Finally, experience with a computer-based diagnostic system using the same system of agreed definitions showed the maximum diagnostic error rate due to faulty acquisition of data to be low (4.7°/o in a series of 552 cases). It is suggested as a result of these studies that — at least in respect of abdominal pain — errors in data acquisition by the clinician need not be the prime cause of faulty diagnoses.

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