International Journal of Computer and Applications

Texture features for image classification and retrieval.

Ultrasound (US) imaging is the most commonly performed cross-sectional diagnostic imaging modality in the practice of medicine. It is low-cost, non-ionizing, portable, and capable of real-time image acquisition and display. US is a rapidly evolving technology with significant challenges and opportunities. Challenges include high inter- and intra-operator variability and limited image quality control. Tremendous opportunities have arisen in the last decade as a result of exponential growth in available computational power coupled with progressive miniaturization of US devices. As US devices become smaller, enhanced computational capability can contribute significantly to decreasing variability through advanced image processing. In this paper, we review leading machine learning (ML) approaches and research directions in US, with an emphasis on recent ML advances. We also present our outlook on future opportunities for ML techniques to further improve clinical workflow and US-based disease diagnosis and characterization.

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Machine learning for medical ultrasound: status, methods, and future opportunities.

This work aims at designing a lightweight convolutional neural network for image super resolution (SR). With simplicity bare in mind, we construct a pretty concise and effective network with a newly proposed pixel attention scheme. Pixel attention (PA) is similar as channel attention and spatial attention in formulation. The difference is that PA produces 3D attention maps instead of a 1D attention vector or a 2D map. This attention scheme introduces fewer additional parameters but generates better SR results. On the basis of PA, we propose two building blocks for the main branch and the reconstruction branch, respectively. The first one - SC-PA block has the same structure as the Self-Calibrated convolution but with our PA layer. This block is much more efficient than conventional residual/dense blocks, for its twobranch architecture and attention scheme. While the second one - UPA block combines the nearest-neighbor upsampling, convolution and PA layers. It improves the final reconstruction quality with little parameter cost. Our final model- PAN could achieve similar performance as the lightweight networks - SRResNet and CARN, but with only 272K parameters (17.92% of SRResNet and 17.09% of CARN). The effectiveness of each proposed component is also validated by ablation study. The code is available at this https URL.

Efficient Image Super-Resolution Using Pixel Attention

This paper reviewed the 3rd NTIRE challenge on single-image super-resolution (restoration of rich details in a low-resolution image) with a focus on proposed solutions and results. The challenge had 1 track, which was aimed at the real-world single image super-resolution problem with an unknown scaling factor. Participants were mapping low-resolution images captured by a DSLR camera with a shorter focal length to their high-resolution images captured at a longer focal length. With this challenge, we introduced a novel real-world super-resolution dataset (RealSR). The track had 403 registered participants, and 36 teams competed in the final testing phase. They gauge the state-of-the-art in real-world single image super-resolution.

/pdf/ntire-2019-challenge-on-real-image-super-resolution-methods-3qgjnvehdu.pdf

NTIRE 2019 Challenge on Real Image Super-Resolution: Methods and Results

Image Classification and Retrieval

Medical Diagnosis has been gaining importance in everyday life. The diseases and their symptoms are highly varying and there is always a need for a continuous update of knowledge needed for the doctors. This forces lots of challenges as the diagnostic tools need to visualize organs and soft tissues and further classify them for diagnosis. One such application of diagnostic ultrasound is liver imaging. The existing approaches for classification & retrieval system have the following issues: speckle noise, semantic gap, computational time, dimensionality reduction and accuracy of retrieved images from large dataset. This paper proposes a new method for the classification & retrieval of liver diseases from ultrasound image dataset. The proposed work concentrates on diagnosing both focal and diffuse liver diseases from ultrasound images. The contribution of this paper relies on the following areas. Speckle reduction by Modified Laplacian Pyramid Nonlinear Diffusion (MLPND), Mutual Information (MI) based image registration, Image texture analysis by Haralick’s features, Image Classification & retrieval by machine learning algorithms. The dataset used in each phase of the work are authenticated dataset provided by doctors. The results at each phase have been evaluated with doctors in the relevant field.

Classification and Retrieval of Focal and Diffuse Liver from Ultrasound Images Using Machine Learning Techniques

Medical Images have been widely used to diagnose and detect the abnormalities in the human body. In this study, we have focused on ECG (Electrocardiography) signals which play a vital role in diagnosing Cardio Vascular Disease (CVD). CVD is most prevalent among the people because of changing lifestyle, Age, Tobacco, etc. According to the WHO (World Health Organization) report in 2012, around 17 million people die each year because of CVD, which represents 31% of the overall death rate. Due to the advancement in the e-Health care system, most of the ECG signals along with the patient's sensitive data are transmitted among doctors and hospitals for detailed analysis. According to the HIPAA act, it should be made sure that patient's details are to be sent securely during the transmission. In this study various ECG Steganography techniques that have been used by researchers are discussed; in which patient details and diagnosis report are embedded into ECG signals.

Steganography techniques for ECG signals : A survey

Classification is considered to be the one of the data analysis technique which can be used over many applications. Classification model predicts categorical continuous class labels. Clustering mainly deals with grouping of variables based upon similar characteristics. Classification models are experienced by comparing the predicted values to that of the known target values in a set of test data. Data classification has many applications in business modeling, marketing analysis, credit risk analysis; biomedical engineering and drug retort modeling. The extension of data analysis and classification makes the insight into big data with an exploration to processing and managing large data sets. This chapter deals with various techniques, methodologies that correspond to the classification problem in data analysis process and its methodological impacts to big data.

Data Classification: Its Techniques and Big Data

COVID-19 is an infectious disease caused by a novel coronavirus that affects most of the people around the world in their respiratory system. The COVID-19 crisis has affected the married Indian women in a different way and overloads them in all aspects during the quarantine period. Across every domain, from health to the economy, safety to societal protection, education to occupation, this creates a greater impact irrespective of their professions. Psychological and economic impacts are felt especially by working women who are getting moderate and low income and close to the poverty line. Unpaid work has amplified at home, with children out of school, spouse work from home, intensified special care needs of elder persons, and other emotional uncertainties. To explore various impacts of lockdown due to COVID-19, this study analyzes the challenges and opportunities of a married woman with a special reference to TIER-II selected city in India.

R. Suganya

Papers

Image Classification and Retrieval

Classification and Retrieval of Focal and Diffuse Liver from Ultrasound Images Using Machine Learning Techniques

Steganography techniques for ECG signals : A survey

Data Classification: Its Techniques and Big Data

Effect of COVID-19 quarantine period on a married woman with special reference to a TIER-II selected City in India