U-net is an image segmentation technique developed primarily for image segmentation tasks. These traits provide U-net with a high utility within the medical imaging community and have resulted in extensive adoption of U-net as the primary tool for segmentation tasks in medical imaging. The success of U-net is evident in its widespread use in nearly all major image modalities, from CT scans and MRI to X-rays and microscopy. Furthermore, while U-net is largely a segmentation tool, there have been instances of the use of U-net in other applications. Given that U-net’s potential is still increasing, this narrative literature review examines the numerous developments and breakthroughs in the U-net architecture and provides observations on recent trends. We also discuss the many innovations that have advanced in deep learning and discuss how these tools facilitate U-net. In addition, we review the different image modalities and application areas that have been enhanced by U-net.

U-Net and Its Variants for Medical Image Segmentation: A Review of Theory and Applications

https://repositorio-aberto.up.pt/bitstream/10216/124569/3/369051.1.pdf

Deep-learning framework to detect lung abnormality - A study with chest X-Ray and lung CT scan images

Tuberculosis (TB) is a chronic lung disease that occurs due to bacterial infection and is one of the top 10 leading causes of death. Accurate and early detection of TB is very important, otherwise, it could be life-threatening. In this work, we have detected TB reliably from the chest X-ray images using image pre-processing, data augmentation, image segmentation, and deep-learning classification techniques. Several public databases were used to create a database of 3500 TB infected and 3500 normal chest X-ray images for this study. Nine different deep CNNs (ResNet18, ResNet50, ResNet101, ChexNet, InceptionV3, Vgg19, DenseNet201, SqueezeNet, and MobileNet) were used for transfer learning from their pre-trained initial weights and were trained, validated and tested for classifying TB and non-TB normal cases. Three different experiments were carried out in this work: segmentation of X-ray images using two different U-net models, classification using X-ray images and that using segmented lung images. The accuracy, precision, sensitivity, F1-score and specificity of best performing model, ChexNet in the detection of tuberculosis using X-ray images were 96.47%, 96.62%, 96.47%, 96.47%, and 96.51% respectively. However, classification using segmented lung images outperformed that with whole X-ray images; the accuracy, precision, sensitivity, F1-score and specificity of DenseNet201 were 98.6%, 98.57%, 98.56%, 98.56%, and 98.54% respectively for the segmented lung images. The paper also used a visualization technique to confirm that CNN learns dominantly from the segmented lung regions that resulted in higher detection accuracy. The proposed method with state-of-the-art performance can be useful in the computer-aided faster diagnosis of tuberculosis.

Reliable Tuberculosis Detection Using Chest X-Ray With Deep Learning, Segmentation and Visualization

Medical data is one of the most rewarding and yet most complicated data to analyze. How can healthcare providers use modern data analytics tools and technologies to analyze and create value from complex data? Data analytics, with its promise to efficiently discover valuable pattern by analyzing large amount of unstructured, heterogeneous, non-standard and incomplete healthcare data. It does not only forecast but also helps in decision making and is increasingly noticed as breakthrough in ongoing advancement with the goal is to improve the quality of patient care and reduces the healthcare cost. The aim of this study is to provide a comprehensive and structured overview of extensive research on the advancement of data analytics methods for disease prevention. This review first introduces disease prevention and its challenges followed by traditional prevention methodologies. We summarize state-of-the-art data analytics algorithms used for classification of disease, clustering (unusually high incidence of a particular disease), anomalies detection (detection of disease) and association as well as their respective advantages, drawbacks and guidelines for selection of specific model followed by discussion on recent development and successful application of disease prevention methods. The article concludes with open research challenges and recommendations.

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Big data analytics for preventive medicine

Active deep neural network features selection for segmentation and recognition of brain tumors using MRI images

Deep Learning for Medical Image Processing

https://repositorio-aberto.up.pt/bitstream/10216/122067/2/348986.1.pdf

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

A Novel Fusion Approach for Early Lung Cancer Detection Using Computer Aided Diagnosis Techniques

Recent advances in Big Data Analytics, Internet of Things and Machine Learning

Computerized disease examination techniques are widely adopted in the literature to evaluate a considerable number of medical images ranging from the RGB scale to gray scale. This work proposes a novel image extraction method by combining Kapur’s thresholding and Hough transform (HT) to extract the leukocyte segment from the RGB-scaled blood smear image (BSI). Automated mining of the leukocyte region is always preferred in medical clinics for fast disease examination and treatment for the planning process. This study aims to implement a hybrid procedure to extort the leukocyte segment. Kapur’s is considered to enhance the RGB-scaled test image, and the HT is used to detect and extract the circle section from the image. In this work, the hematological images of leukocyte images for segmentation and classification (LISC) database are adopted for the examination. The extracted leukocyte picture is then evaluated with ground truth, and the essential image performance parameters (IPP) are then computed. This work is then validated against the semiautomated approaches, such as Shannon’s entropy-based Chan-Vese and level-set segmentation techniques existing in the literature. The outcome of the proposed techniques confirms that proposed procedure gives better IPP values compared to the existing semiautomated techniques.

Hong Lin

Papers

Deep Learning for Medical Image Processing

Social-Group-Optimization based tumor evaluation tool for clinical brain MRI of Flair/diffusion-weighted modality

A Novel Fusion Approach for Early Lung Cancer Detection Using Computer Aided Diagnosis Techniques

Recent advances in Big Data Analytics, Internet of Things and Machine Learning

Firefly Algorithm-Based Kapur’s Thresholding and Hough Transform to Extract Leukocyte Section from Hematological Images