The expansion of automation techniques and increased risk of identity theft have led emphasis on the tremendous need of automated identification system. Due to the high recognition accuracy and robustness to changes in human physiology, retinal biometric identification system has drawn much attention in this research field. In this paper, we aim to propose an automatic fast and accurate retinal identification system for the multi-sample data set. The proposed approach uses a hybrid segmentation technique to segment out both thick/thin vessels for effectively balancing the difference of wavelet response between thick/thin blood vessels. As a result, recognition accuracy is improved. A PCA (Principle Component Analysis) based feature processing approach is proposed for efficiently reducing the dimensionality of a large number of vessels features. It significantly reduces computation time and accelerates the matching process in the retinal identification system. The proposed technique is validated on DRIVE, STARE, VARIA, RIDB, HRF, Messidor, DIARETDB0, and a large multi-sample per subject database created by authors using the images provided by Dr. Chen (Shanghai Jiao Tong University Affiliated Sixth People Hospital). Experimental results demonstrated that the proposed approach outperforms other existing techniques. Segmentation achieves an overall accuracy of 99.65% with the recognition rate of 99.40% on all these databases.
Objectives: To compare the recurrence rate between incision drainage andmultiple needle aspiration for breast abscess treatment. Study Design: Randomized ControlledTrial. Setting: Department of General Surgery, Bahawal Victoria Hospital, Bahawalpur. StudyDuration: 29th September 2015 to 29th June 2016. Materials & Methods: A total of 60 femalepatients with breast abscess of <2 cm in size and of duration <2 weeks between 20 to 40 yearsof age were included. Patients with multiple breast abscesses, recurrent breast abscesses andcomplicated abscesses were excluded. The patients were randomized into Group A (incisionand drainage) & Group B (needle aspiration), by using lottery method. Follow up was done forup to 7 days and recurrence was noted. Results: The mean age of patients in group A was30.83 ± 5.67 years and in group B was 31.53 ± 5.73 years. Mean duration of disease was 7.58± 2.83 days. Mean size of abscess was 0.86 ± 0.43 cm. Recurrence was found in 07 (23.33%)patients in group A (incision drainage) while in 21 (70.0%) patients in group B (multiple needleaspiration) with p-value of 0.000 which is statistically significant. Conclusion: The recurrencerate is less after incision & drainage as compared to multiple needle aspirations for treatingbreast abscess.
Over the years, the paradigm of medical image analysis has shifted from manual expertise to automated systems, often using deep learning (DL) systems. The performance of deep learning algorithms is highly dependent on data quality. Particularly for the medical domain, it is an important aspect as medical data is very sensitive to quality and poor quality can lead to misdiagnosis. To improve the diagnostic performance, research has been done both in complex DL architectures and in improving data quality using dataset dependent static hyperparameters. However, the performance is still constrained due to data quality and overfitting of hyperparameters to a specific dataset. To overcome these issues, this paper proposes random data augmentation based enhancement. The main objective is to develop a generalized, data-independent and computationally efficient enhancement approach to improve medical data quality for DL. The quality is enhanced by improving the brightness and contrast of images. In contrast to the existing methods, our method generates enhancement hyperparameters randomly within a defined range, which makes it robust and prevents overfitting to a specific dataset. To evaluate the generalization of the proposed method, we use four medical datasets and compare its performance with state-of-the-art methods for both classification and segmentation tasks. For grayscale imagery, experiments have been performed with: COVID-19 chest X-ray, KiTS19, and for RGB imagery with: LC25000 datasets. Experimental results demonstrate that with the proposed enhancement methodology, DL architectures outperform other existing methods. Our code is publicly available at: https://github.com/aleemsidra/Augmentation-Based-Generalized-Enhancement.
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