These days, deep learning and computer vision are much-growing fields in this modern world of information technology. Deep learning algorithms and computer vision have achieved great success in different applications like image classification, speech recognition, self-driving vehicles, disease diagnostics, and many more. Despite success in various applications, it is found that these learning algorithms face severe threats due to adversarial attacks. Adversarial examples are inputs like images in the computer vision field, which are intentionally slightly changed or perturbed. These changes are humanly imperceptible. But are misclassified by a model with high probability and severely affects the performance or prediction. In this scenario, we present a deep image restoration model that restores adversarial examples so that the target model is classified correctly again. We proved that our defense method against adversarial attacks based on a deep image restoration model is simple and state-of-the-art by providing strong experimental results evidence. We have used MNIST and CIFAR10 datasets for experiments and analysis of our defense method. In the end, we have compared our method to other state-ofthe-art defense methods and proved that our results are better than other rival methods.
Objectives: To determine the recurrence rate of Stricture Urethra following Optical Urethrotomy in department of Urology at people’s medical college hospital Nawabshah, a 2 years’ experience. Study Design: Prospective observational. Setting: Department of Urology at People’s Medical College Hospital Nawabshah. Period: January 2016 to January 2018. Methodology: Patients who fulfill inclusion criteria were admitted through Urology OPD. An informed consent was taken. All baseline investigations / Antegrade and Retrograde Urethrogram, Qmax in uroflowmetery, post void residual ultrasound scan were performed in all cases. The patients were asked to attend the OT after anesthetic assessment, under spinal anesthesia. They were advised to have follow-up visits with uroflowmetery and PVR. All the collected data was filled on Performa. Data was analyzed through SPSS Version 20.0. Results: A total of 95 patients (100 %) underwent first session of DVIU, out of 95 patients 37 patients (38.95 %) showed improvement in subjective, while remaining 58 patients (61.05%) showed deterioration. so they underwent second session of DVIU. After second session of DVIU 15 patients (25.86%) out of remaining 58 patients showed improvement, while 43 patients (74.14%) remained in agony, So I counseled them all (remaining 43 patients) for third sitting of DVIU or open urethroplasty. Out of 43 remaining patients only 23 patients willingly underwent third session of DVIU and remaining 20 patients refused and they directly underwent open end to end urethroplasty. The 23 patients, who underwent DVIU, have failed and finally they also underwent urethroplasty. Conclusion: The recurrence rate after DVIU has based on multiple factors that should be properly addressed during treatment planning to avoid unnecessary re treatment, to decrease the rate of more invasive open surgical procedure.
Brain magnetic resonance images (MRI) are used to diagnose the different diseases of the brain, such as swelling and tumor detection. The quality of the brain MR images is degraded by different noises, usually salt & pepper and Gaussian noises, which are added to the MR images during the acquisition process. In the presence of these noises, medical experts are facing problems in diagnosing diseases from noisy brain MR images. Therefore, we have proposed a de-noising method by mixing concatenation, and residual deep learning techniques called the MCR de-noising method. Our proposed MCR method is to eliminate salt & pepper and gaussian noises as much as possible from the brain MRI images. The MCR method has been trained and tested on the noise quantity levels 2% to 20% for both salt & pepper and gaussian noise. The experiments have been done on publically available brain MRI image datasets, which can easily be accessible in the experiments and result section. The Structure Similarity Index Measure (SSIM) and Peak Signal-to-Noise Ratio (PSNR) calculate the similarity score between the denoised images by the proposed MCR method and the original clean images. Also, the Mean Squared Error (MSE) measures the error or difference between generated denoised and the original images. The proposed MCR denoising method has a 0.9763 SSIM score, 84.3182 PSNR, and 0.0004 MSE for salt & pepper noise; similarly, 0.7402 SSIM score, 72.7601 PSNR, and 0.0041 MSE for Gaussian noise at the highest level of 20% noise. In the end, we have compared the MCR method with the state-of-the-art de-noising filters such as median and wiener de-noising filters.
Urdu is a widely spoken and narrated language in several South-Asian countries and communities worldwide. It is relatively hard to recognize Urdu text compared to other languages due to its cursive writing style. The Urdu text script belongs to a non-Latin cursive family script like Arabic, Hindi and Chinese. Urdu is written in several writing styles, among which `Nastaleeq’ is the most popular and widely used font style. A gap still poses a challenge for localization/detection and recognition of Urdu Nastaleeq text as it follows modified version of Arabic script. This research study presents a methodology to recognize and classify Urdu text in Nastaleeq font, regardless of the text position in the image. The proposed solution is comprised of a two-step methodology. In the first step, text detection is performed using the Connected Component Analysis (CCA) and Long Short-Term Memory Neural Network (LSTM). In the second step, a hybrid Convolution Neural Network and Recurrent Neural Network (CNN-RNN) architecture is deployed to recognize the detected text. The image containing Urdu text is binarized and segmented to produce a single-line text image fed to the hybrid CNN-RNN model, which recognizes the text and saves it in a text file. The proposed technique outperforms the existing ones by achieving an overall accuracy of 97.47%.
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