BACKGROUND Breast carcinoma is one of the leading causes of excess mortality rates in Harlem, an inner‐city neighborhood with the highest mortality rates and worst life expectancy in New York City. This study reports the results of a breast carcinoma screening and diagnostic program in Harlem. METHODS Retrospective review was performed of a database of 49,750 visits to the Breast Examination Center of Harlem from 1995 to 2000. During this period, 181 breast carcinomas were diagnosed in 178 women. The medical records of these 178 women were reviewed to determine the method of detection, stage, and treatment. RESULTS Among these women, 89% were black or Hispanic, 45% had no medical insurance, and 38% had incomes below federal poverty guidelines. Breast carcinoma stage, known for 167 carcinomas, was Stage 0 in 38 (23%), Stage I in 38 (23%), Stage II in 63 (38%), Stage III in 24 (14%), and Stage IV in 4 (2%). Fifty‐six cases (34%) were minimal breast carcinomas. Of 181 breast carcinomas, 122 (67%) were palpable and 59 (33%) were nonpalpable, detected only by mammography in asymptomatic women. Nonpalpable, as opposed to palpable, breast carcinomas were significantly more likely to be ductal carcinoma in situ (30 of 55 [54%] vs. 8 of 112 [7%], P < 0.0000001) or minimal breast carcinoma (39 of 55 [71%] vs. 17 of 112 [15%], P = 0.0000001) and were more likely to be treated with breast‐conserving surgery (47 of 56 [84%] vs. 76 of 110 [69%], P < 0.04). CONCLUSIONS A breast carcinoma screening and diagnostic program has been established in Harlem, a traditionally underserved area in New York City. Early, curable breast carcinomas were detected but outreach remains a challenge, particularly for the uninsured. Cancer 2002;95:8–14. © 2002 American Cancer Society. DOI 10.1002/cncr.10640
Degrading Air Quality is a major concern for all species on this planet. Over the years, it is seen that air quality is constantly degrading mainly because of the reasons such as industrialisation, deforestation, and green-house effect. Main parameters to be considered for the Air Quality are the Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols. A study of these parameters changing over time is necessary so to keep a check on the degrading air quality. In this study, the data of Carbon Monoxide (CO), Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3) and Aerosols are taken for the past 5 years i.e. 2018 to 2022 and their time series is extracted thereafter a test on stationarity is done so as to know whether these series are stationary or not. Two machine learning models namely Holt winter’s Smoothing and FbProphet is applied to predict the value adjacent to the original value and a error metric is comparison is done to find out which model is best suited for forecasting these Air Quality parameters.
Edge Detection is one of the most essential steps for image processing to identify and detect discontinuity in intensity variation. It is an effective and an efficient tool to recognize different properties of an image such as shape, contrast, color, scene analysis, image segmentation etc. The technique is very important to recognize all the edges accurately. It helps in object recognition, pattern recognition, medical image processing, motion analysis etc. There are many edge detection operators available in image processing. This paper illustrates the performance analysis of the most commonly used edge detection techniques including Canny, Sobel and Prewitt, highlighting their advantages and disadvantages with respect to different types of datasets. After analyzing various parameters like Accuracy, Mean Square Error (MSE), Peak Signal to Noise Ratio (PSNR), Edge Detection Processing Time and Qualitative Human Visual Perception on two diverse type of datasets, varied results are found with respect to the techniques used. Among them, the most accurate and fast computed edge detection technique which gives better results on both type of datasets is concluded. Although the Sobel edge detection technique gives relatively poor result and weak performance of detection of edges, however it can be modified and further improved with respect to future work. The entire analyzing process was done under Scilab software.
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