Lung Diseases Detection Using Various Deep Learning Algorithms.

The primary objective of this proposed framework work is to detect and classify various lung diseases such as pneumonia, tuberculosis, and lung cancer from standard X-ray images and Computerized Tomography (CT) scan images with the help of volume datasets. We implemented three deep learning models namely Sequential, Functional & Transfer models and trained them on open-source training datasets. To augment the patient's treatment, deep learning techniques are promising and successful domains that extend the machine learning domain where CNNs are trained to extract features and offers great potential from datasets of images in biomedical application. Our primary aim is to validate our models as a new direction to address the problem on the datasets and then to compare their performance with other existing models. Our models were able to reach higher levels of accuracy for possible solutions and provide effectiveness to humankind for faster detection of diseases and serve as best performing models. The conventional networks have poor performance for tilted, rotated, and other abnormal orientation and have poor learning framework. The results demonstrated that the proposed framework with a sequential model outperforms other existing methods in terms of an F1 score of 98.55%, accuracy of 98.43%, recall of 96.33% for pneumonia and for tuberculosis F1 score of 97.99%, accuracy of 99.4%, and recall of 98.88%. In addition, the functional model for cancer outperformed with an accuracy of 99.9% and specificity of 99.89% and paves way to less number of trained parameters, leading to less computational overhead and less expensive than existing pretrained models. In our work, we implemented a state-of-the art CNN with various models to classify lung diseases accurately.

Flexible, Highly Sensitive Paper-Based Screen Printed MWCNT/PDMS Composite Breath Sensor for Human Respiration Monitoring.

Accurate measurement and monitoring of respiration is vital in patients affected by severe acute respiratory syndrome coronavirus - 2 (SARS-CoV-2). Patients with severe chronic diseases and pneumonia need continuous respiration monitoring and oxygenation support. Existing respiratory sensing techniques require direct contact with the human body along with expensive and heavy Holter monitors for continuous real-time monitoring. In this work, we propose a low-cost, non-invasive and reliable paper-based wearable screen printed sensor for human respiration monitoring as an effective alternative of existing sensing systems. The proposed sensor was fabricated using traditional screen printing of multi-walled carbon nanotubes (MWCNTs) and polydimethylsiloxane (PDMS) composite based interdigitated electrodes on paper substrate. The paper substrate was used as humidity sensing material of the sensor. The hygroscopic nature of paper during inhalation and exhalation causes a change in dielectric constant, which in turn changes the capacitance of the sensor. The composite interdigitated electrode configuration exhibited better response times with a rise time of 1.178s being recorded during exhalation and fall time of 0.88s during inhalation periods. The respiration rate of sensor was successfully examined under various breathing conditions such as normal breathing, deep breathing, workout, oral breathing, nasal breathing, fast breathing and slow breathing by employing it in a wearable mask, a mandatory wearable product during the current COVID-19 pandemic situation.Thus, the above proposed sensor may hold tremendous potential in wearable/flexible healthcare technology with good sensitivity, stability, biodegradability and flexibility at this time of need.