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SEHAT (Smart & Economical Healthcare using Advanced Technology) is being developed for the inexpensive consultancy about your health monitoring & treatment of diseases while at home from the well known, reliable, reputed and experienced medical doctors.
Features
Almost FREE consultancy for financially deserving patients
No chance of fake or incompetent doctor due to the strict policy
24/7 round-the-clock service
Face-to-face chatting
The medical history of the patient is maintained and available to any authorised doctor
Coming soon...
Payment via popular channels like easy paisa, jazz cash, bank card.
Bilingual (English, Urdu) support.
Availability of well known degree holder Nutritionist, Physiotherapist, Psychologist, Hakeem, and Homeopathic specialist
Verified collection of health tips
Guidance for the suitable medical specialist
Auto alert when your required doctor is available
Asynchronous treatment: Submit your health problem via text or voice or upload lab reports to your doctor to get the transcript of doctor when available.
Auto conversion of prescribed medicine general names into chemical names along with the alternate brands starting from the lowest price.
AI Predicts Tongue Disease with 96% Accuracy
A new study shows how artificial intelligence is transforming the way doctors diagnose tongue-related diseases.
This AI-driven tool analyzes images of the tongue to detect various health conditions based on color.
By training on thousands of images, the system can identify diseases such as diabetes, asthma, and even COVID-19 by examining the color and other features of the tongue.
Our Research
Sara Younas, Allah Bux Sargano, Lihua You, Zulfiqar Habib (2025), "Attention-Based Inception-Residual CNN: Skin Cancer Diagnosis with Attention-Based Inception-Residual CNN Model", Information, pp: 120, Vol: 16, Issue: 2, ISSN: 2078-2489 (Online), DOI: https://doi.org/10.3390/info16020120, Impact Factor: 2.4
Shehroz Tariq, Rehan Raza, Allah Bux Sargano, Zulfiqar Habib (2024), "CEV-BCA: Convolutional Enhanced Vision Transformer for Magnification Independent Breast Cancer Analysis from Histopathological Images", Multimedia Tools and Applications, ISSN: 1380-7501 (Print), 1573-7721 (Online), DOI: https://doi.org/10.1007/s11042-024-19685-9, Impact Factor: 3.6
Muhammad Nadeem Ashraf, Muhammad Hussain, Zulfiqar Habib (2022), "Deep Red Lesions Classification for Early Screening of Diabetic Retinopathy", Mathematics: Special Issue “Computer Graphics, Image Processing and Artificial Intelligence", pp: 686, Vol: 10, Issue: 5, ISSN: 2227-7390 (Online), DOI: https://doi.org/10.3390/math10050686, Impact Factor: 2.258
Rehan Raza, Fatima Zulfiqar, Shehroz Tariq, Gull Bano Anwar, Allah Bux Sargano, Zulfiqar Habib (2021), "Melanoma Classification from Dermoscopy Images Using Ensemble of Convolutional Neural Networks", Mathematics: Special Issue Computer Graphics, Image Processing and Artificial Intelligence, pp: 26, Vol: 10, Issue: 1, ISSN: 2227-7390 (Online), DOI: https://doi.org/10.3390/math10010026, Impact Factor: 2.258
Muhammad Nadeem Ashraf, Muhammad Hussain, Zulfiqar Habib (2020), "Review of various tasks performed in the preprocessing phase of a Diabetic Retinopathy Diagnosis System", Current Medical Imaging, pp: 397-426, Vol: 16, Issue: 4, Standard: ISSN: 1875-6603 (Online), 1573-4056 (Print), Impact Factor: 0.533
Ghulam Gilanie, Usama Ijaz Bajwa, Mustansar Mahmood Waraich, Zulfiqar Habib (2019), "Automated and Reliable Brain Radiology with Texture Analysis of MRI and Cross Dataset Validation", International Journal of Imaging Systems and Technology, pp: 531-538, Vol: 29, Issue: 4, ISSN: 1098-1098 (Online), DOI: https://doi.org/10.1002/ima.22333, Impact Factor: 1.254
Ghulam Gilanie, Usama Ijaz Bajwa, Mustansar Mahmood Waraich, Zulfiqar Habib (2019), "Computer Aided Diagnosis of Brain Abnormalities Using Texture Analysis of MRI Images", International Journal of Imaging Systems and Technology, pp: 1-12, Standard: 1098-1098 (Online), Impact Factor: 1.423
Ghulam Gilanie, Usama Ijaz Bajwa, Mustansar Mahmood Waraich, Zulfiqar Habib, Hafeez Ullah, Muhammad Nasir (2018), "Classification of Normal and Abnormal Brain MRI Slices using Gabor Texture and Support Vector Machines", Signal, Image and Video Processing, pp: 479-487, Vol: 12, Issue: 3, Standard: 1863-1703 (Print), 1863-1711 (Online), Impact Factor: 1.894
Ghulam Gilanie, Hafeez Ullah, Mustansar Mehmood, Usama Ejaz, Zulfiqar Habib (2018), "Colored Representation of Brain Gray Scale MRI Images to potentially underscore the variability and sensitivity of images", Current Medical Imaging Reviews, pp: 555-560, Vol: 14, Issue: 4, Standard: 1875-6603 (Online), 1573-4056 (Print), Impact Factor: 0.533
Tools & Technologies
Frontend:
Built using Next.js 15, React 18, and TypeScript for fast, secure, and scalable performance.
UI & Styling: Tailwind CSS for responsive design, Framer Motion for animations, and Lucide React icons.
State Management: Redux Toolkit, React Query, and Context API for real-time data and app control.
Authentication: Firebase for users and custom JWT for admin security.
PWA Support: Offline use, background sync, and installable mobile app via next-pwa.
Utilities: React Hook Form for forms, date-fns for date handling, Recharts for analytics, and Cloudinary for file uploads.
Backend:
Developed with Node.js, Express.js, and TypeScript for a type-safe, modular backend.
Databases: MySQL (with Prisma ORM) for structured data and MongoDB (with Mongoose ODM) for unstructured records.
Features: RESTful APIs, schema migrations, validation, and secure authentication middleware.
Overall, SEHAT combines AI-powered eHealthcare with a strong technical base for reliability, scalability, and data protection.
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