Foundations of Deep Learning and Its Applications to Health Informatics

Three‐dimensional (3D) image reconstruction of tumours can visualise their structures with precision and high resolution. In this article, GAN‐LSTM‐3D method is proposed for 3D reconstruction of lung cancer tumours from 2D CT images. Our method consists of three phases: lung segmentation, tumour segmentation, and tumour 3D reconstruction. Lung segmentation is done using snake optimisation followed by tumour segmentation using Gustafson‐Kessel (GK) clustering method. The outputs of GK (2D lung cancer images) are fed to a pre‐trained Visual Geometry Group (VGG) for feature extraction. The VGG outputs are used as input for an attention‐based LSTM which performs feature unpacking. The output of LSTM units is given to generator network of a Generative Adversarial Networks (GAN) model to carry out 3D reconstruction of (normal/cancerous) images with high quality. During training, the discriminator network of the GAN is used to judge the generator outputs. The authors to the best of their knowledge were the first to use GAN for 3D reconstruction of lung cancer tumours which is the primary contribution of this article. Moreover, existing studies are mostly focused on brain tumours and are not suitable for lung tumour reconstruction. Focusing on lung tumours is the second contribution of this article. Evaluation on LUNA data collection against existing methods like MC, MC + fairing etc. reveals the superiority of our method in terms of Hamming and Euclidean distance metrics. Additionally, the computational complexity of the proposed method is lower compared to evaluated methods.

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Human Activity Tracking via Machine Learning Methods in a Smart Healthcare Informatics

Gautam,

Singh,

Jermsittiparsert

et al. 2024

Advances in Human and Social Aspects of Technology

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The new era of “smart health” has begun as a result of advances in advanced technology, revolutionizing patient and medical staff healthcare procedures both inside and outside of hospitals. Patients can now gather thorough health data utilizing cellphones and wearable technology, independent of their geographic location or time zone. However, present tactics for smart-health devices tend to focus on the smartness of the technology itself and on controlling a specific disease or condition. The use of this patient-generated data holds great promise for future healthcare breakthroughs in many ways. This chapter focuses on the Human Activity Tracking via Machine Learning Methods in a Smart Healthcare Informatics in which Legal Challenges, Security Issues, Opportunities and Applications towards Smart Monitoring and Diagnosis Technologies.

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Foundations of Deep Learning and Its Applications to Health Informatics

Cited by 3 publications

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Deep Learning Based Obstructive Sleep Apnea Detection for e-health Applications

Deep Learning Based Obstructive Sleep Apnea Detection for e-health Applications

GAN‐LSTM‐3D: An efficient method for lung tumour 3D reconstruction enhanced by attention‐based LSTM

Human Activity Tracking via Machine Learning Methods in a Smart Healthcare Informatics

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