ABCanDroid: A Cloud Integrated Android App for Noninvasive Early Breast Cancer Detection Using Transfer Learning.

Many patients affected by breast cancer die every year because of improper diagnosis and treatment. In recent years, applications of deep learning algorithms in the field of breast cancer detection have proved to be quite efficient. However, the application of such techniques has a lot of scope for improvement. Major works have been done in this field, however it can be made more efficient by the use of transfer learning to get impressive results. In the proposed approach, Convolutional Neural Network (CNN) is complemented with Transfer Learning for increasing the efficiency and accuracy of early detection of breast cancer for better diagnosis. The thought process involved using a pre-trained model, which already had some weights assigned rather than building the complete model from scratch. This paper mainly focuses on ResNet101 based Transfer Learning Model paired with the ImageNet dataset. The proposed framework provided us with an accuracy of 99.58%. Extensive experiments and tuning of hyperparameters have been performed to acquire the best possible results in terms of classification. The proposed frameworks aims to be an efficient tool for all doctors and society as a whole and help the user in early detection of breast cancer.

Federated learning based Covid-19 detection.

The world is affected by COVID-19, an infectious disease caused by the SARS-CoV-2 virus. Tests are necessary for everyone as the number of COVID-19 affected individual's increases. So, the authors developed a basic sequential CNN model based on deep and federated learning that focuses on user data security while simultaneously enhancing test accuracy. The proposed model helps users detect COVID-19 in a few seconds by uploading a single chest X-ray image. A deep learning-aided architecture that can handle client and server sides efficiently has been proposed in this work. The front-end part has been developed using StreamLit, and the back-end uses a Flower framework. The proposed model has achieved a global accuracy of 99.59% after being trained for three federated communication rounds. The detailed analysis of this paper provides the robustness of this work. In addition, the Internet of Medical Things (IoMT) will improve the ease of access to the aforementioned health services. IoMT tools and services are rapidly changing healthcare operations for the better. Hopefully, it will continue to do so in this difficult time of the COVID-19 pandemic and will help to push the envelope of this work to a different extent.

Listen to my story: Contribution of patients to their healthcare through effective communication with doctors.

In the era of patient centered healthcare, patients are educated, more aware and demanding than ever. However, there is a significant misalignment between patients and doctors due to improper communication resulting in broken patient-doctor therapeutic relationships and degraded quality of healthcare. This suggests that patients have a greater and mature role to play in their healthcare. The paper aims to fill this gap by studying the contribution of patients in their healthcare through patientdoctor communication in selected Indian multispeciality hospitals. Qualitative multi-case study was steered and in-depth interviews of thirteen patients, twelve doctors were conducted along with the secondary data analysis of more than 600 pages of the documents from the official websites of the sample hospitals. Grounded theory three level coding revealed the themes of contribution of patients in through effective communication. The results indicate that patients contribute to their healthcare through effective communication by demonstrating association with doctors, reflecting reciprocally, resolving communication challenges and supporting their overall treatment process. The paper extends the literature on patient's contribution in their healthcare. It presents clear and succinct implementable implications and distinctive ways in which patients cooperate with the doctors, work mutually, improves communication and strengthen their overall healthcare process.