A Novel Deep Convolutional Neural Network Architecture Based on Transfer Learning for Handwritten Urdu Character Recognition

Deep convolutional neural networks (CNN) have made a huge impact on computer vision and set the state-of-the-art in providing extremely definite classification results. For character recognition, where the training images are usually inadequate, mostly transfer learning of pre-trained CNN is often utilized. In this paper, we propose a novel deep convolutional neural network for handwritten Urdu character recognition by transfer learning three pre-trained CNN models. We fine-tuned the layers of these pre-trained CNNs so as to extract features considering both global and local details of the Urdu character structure. The extracted features from the three CNN models are concatenated to train with two fully connected layers for classification. The experiment is conducted on UNHD, EMILLE, DBAHCL, and CDB/Farsi dataset, and we achieve 97.18% average recognition accuracy which outperforms the individual CNNs and numerous conventional classification methods

Progression and Challenges of IoT in Healthcare: A Short Review

Smart healthcare, an integral element of connected living, plays a pivotal role in fulfilling a fundamental human need. The burgeoning field of smart healthcare is poised to generate substantial revenue in the foreseeable future. Its multifaceted framework encompasses vital components such as the Internet of Things (IoT), medical sensors, artificial intelligence (AI), edge and cloud computing, as well as next-generation wireless communication technologies. Many research papers discuss smart healthcare and healthcare more broadly. Numerous nations have strategically deployed the Internet of Medical Things (IoMT) alongside other measures to combat the propagation of COVID-19. This combined effort has not only enhanced the safety of frontline healthcare workers but has also augmented the overall efficacy in managing the pandemic, subsequently reducing its impact on human lives and mortality rates. Remarkable strides have been made in both applications and technology within the IoMT domain. However, it is imperative to acknowledge that this technological advancement has introduced certain challenges, particularly in the realm of security. The rapid and extensive adoption of IoMT worldwide has magnified issues related to security and privacy. These encompass a spectrum of concerns, ranging from replay attacks, man-in-the-middle attacks, impersonation, privileged insider threats, remote hijacking, password guessing, and denial of service (DoS) attacks, to malware incursions. In this comprehensive review, we undertake a comparative analysis of existing strategies designed for the detection and prevention of malware in IoT environments.Comment: 7 page

Deep Residual Transfer Learning for Automatic Diabetic Retinopathy Grading.

Evaluation and diagnosis of retina pathology is usually made via the analysis of different image modalities that allow to explore its structure. The most popular retina image method is retinography, a technique that displays the fundus of the eye, including the retina and other structures. Retinography is the most common imaging method to diagnose retina diseases such as Diabetic Retinopathy (DB) or Macular Edema (ME). However, retinography evaluation to score the image according to the disease grade presents difficulties due to differences in contrast, brightness and the presence of artifacts. Therefore, it is mainly done via manual analysis; a time consuming task that requires a trained clinician to examine and evaluate the images. In this paper, we present a computer aided diagnosis tool that takes advantage of the performance provided by deep learning architectures for image analysis. Our proposal is based on a deep residual convolutional neural network for extracting discriminatory features with no prior complex image transformations to enhance the image quality or to highlight specific structures. Moreover, we used the transfer learning paradigm to reuse layers from deep neural networks previously trained on the ImageNet dataset, under the hypothesis that first layers capture abstract features than can be reused for different problems. Experiments using different convolutional architectures have been carried out and their performance has been evaluated on the MESSIDOR database using cross-validation. Best results were found using a ResNet50-based architecture, showing an AUC of 0.93 for grades 0 + 1, AUC of 0.81 for grade 2 and AUC of 0.92 for grade 3 labelling, as well as AUCs higher than 0.97 when considering a binary classification problem (grades 0 vs 3).This work was partly supported by the MINECO/FEDER under TEC2015-64718-R, RTI2018-098913-B-I00, PSI2015-65848-R and PGC2018-098813-B-C32 projects. We gratefully acknowledge the support of NVIDIA Cor poration with the donation of one of the GPUs used for this research. Work by F.J.M.M. was supported by the MICINN “Juan de la Cierva - Formacion” Fellowship

Diabetic Reinopathy Classification using Deep Learning

With diabetes growing at an alarming rate, changes in the retina of diabetic patients causes a condition called diabetic retinopathy which eventually leads to blindness. Early detection of diabetic retinopathy is the best way to provide good timely treatment and thus prevent blindness. Many developed countries have put forward well-structured screening programs which screens every person diagnosed with diabetes at regular intervals. However, the cost of running these programs is increasing with ever increasing disease burden. These screening programs require well trained opticians or ophthalmologist which are expensive especially in developing countries. A global shortage of health care professionals is putting a pressing need to develop fast and efficient screening methods. Using artificial intelligent screening tools will help process and generate a plan for the patients thus skipping the health care provider needed to just classify the disease and will lower the burden on health care professional’s shortage significantly. A plethora of research exists to classify severity of diabetic retinopathy using traditional and end to end methods. In this thesis, we first trained and compared the performance of lightweight architecture MobileNetV2 with other classifiers like DenseNet121 and VGG16 using the Retinal fundus APTOS 2019 Kaggle dataset. We experimented with different image reprocessing techniques and employed various hyperparameter tuning techniques, and found the lightweight architecture MobileNetV2 to give better results in terms of AUC score which defines the ability of the classifier to separate between the classes. We then trained MobileNetV2 using handpicked custom dataset which was an amalgamation of 3 different publicly available datasets viz. the EyePacs Kaggle dataset, the APTOS 2019 Blindness detection dataset and the Messidor2 dataset. We enhanced the retinal features using bio-inspired retinal filters and tuned the hyper-parameters to achieve an accuracy of 91.68% and AUC score of 0.9 when tested on unseen data. The macro precision, recall, and f1-scores are 77.6%, 83.1%, and 80.1% respectively. Our results demonstrate that our computational efficient light weight model achieves promising results and can be deployed as a mobile application for clinical testing