Covid-19 disease Prediction using X-ray images based on AI

•  Detection of Covid-19 and Focusing on Interpretability and Explainability of the Black Box of AI Model. 

• Different image processing techniques were used to improve the quality of the image.

• Densely Connected Squeeze Convolutional Neural Network (DCSCNN) was proposed to classify the lungs diseases.

Object detection using various versions of YOLO Family

• Developed Yolo based detection and classification systems for 

• Spinal bone fracture, 

• Algae detection,

• Other object detection i.e., fire detection, tank detection etc in accordance with different projects requirements.

• Comparison among each model performance.

Idiopathic Pulmonary Fibrosis (IPF) Severity prediction

• The main goal of this project was to develop a machine-learning model to find the severity of IPF disease patients and categorize the patients.

• Multiple machine learning algorithms were used to develop this system .

• Soft voting ensemble approach was adopted for the prediction of IPF disease classification.

Pet Activity Detection for their wellbeing using wearable sensor devices

• The main goal of this project was to develop AI based models that could detect different activities of pets using wearable sensor devices.

• Three sensors were used for this purpose.

• Different AI based algorithms like 1D CNN, LSTM and other models were employed.

Metaverse in Healthcare Integrated with Explainable AI and Blockchain: Enabling Immersiveness, Ensuring Trust, and Providing Patient Data Security

• Recently the world has witnessed the prominence of the metaverse which is an emerging technology in digital space. 

• The metaverse has huge potential to provide a plethora of health services seamlessly to patients and medical professionals with an immersive experience. 

• This project proposes the amalgamation of artificial intelligence and blockchain in the metaverse to provide better, faster, and more secure healthcare facilities in digital space with a realistic experience. 

• Our proposed architecture can be summarized as follows. It consists of three environments, namely the doctor’s environment, the patient’s environment, and the metaverse environment. The doctors and patients interact in a metaverse environment assisted by blockchain technology which ensures the safety, security, and privacy of data. 

• The metaverse environment is the main part of our proposed architecture. The doctors, patients, and nurses enter this environment by registering on the blockchain and they are represented by avatars in the metaverse environment. 

• All the consultation activities between the doctor and the patient will be recorded and the data, i.e., images, speech, text, videos, clinical data, etc., will be gathered, transferred, and stored on the blockchain. 

• These data are used for disease prediction and diagnosis by explainable artificial intelligence (XAI) models. 

• The GradCAM and LIME approaches of XAI provide logical reasoning for the prediction of diseases and ensure trust, explainability, interpretability, and transparency regarding the diagnosis and prediction of diseases. 

• Blockchain technology provides data security for patients while enabling transparency, traceability, and immutability regarding their data. These features of blockchain ensure trust among the patients regarding their data. 

• Consequently, this proposed architecture ensures transparency and trust regarding both the diagnosis of diseases and the data security of the patient. 

• We also explored the building block technologies of the metaverse.