MediLab

Blood Report Form
Report Diagnosis
Login
Diagnosed Reports
Collection Home
MediLocker - Added reports
Services
Home
Signup
Fracture and Tumor Form
Report Upload
not Logged In
Fracture Diagnosis

Inspiration

At MediLab, our inspiration stemmed from the pressing need for swift and precise medical reports and image diagnoses. Recognizing the absence of a readily accessible, global platform for rapid and accurate assessments, we set out to bridge this gap. MediLab is not just another app; it's a website designed to serve users worldwide, ensuring timely and reliable medical insights.

What it does

MediLab is a versatile web-based solution designed to deliver swift and precise diagnoses for medical reports and images. Our platform can analyze X-ray images to discern fractures, distinguish tumor types from scans, and offer concise one-line diagnoses for Blood reports. What sets us apart is our MediLocker, where you can conveniently save or delete reports along with their diagnosis results, putting control in the hands of our valued users.

How we built it

MediLab was meticulously crafted through the integration of cutting-edge medical image processing and web development technologies. Leveraging the power of advanced machine learning, we harnessed the capabilities of HTML, CSS, JavaScript, and Tailwind CSS for our front-end development. Our robust back-end relies on Node.js with Express and a plethora of Node packages for seamless functionality. MongoDB Atlas serves as our trusted database, storing user data and facilitating personalized collection pages.

Our website boasts a range of machine-learning models for radiographic image processing and report analysis. Among these, two pivotal models utilize Convolutional Neural Networks (CNN) – our Fracture Detection model employs VGG19 with batch normalization, while Tumor Analysis features a custom architecture. In addition, we've incorporated Vertex AI which helps to provide diagnosis for Blood Reports.

Challenges we ran into

At MediLab, our key challenge was securing precise diagnoses. We tackled this by experimenting with various models until we achieved at least 90% accuracy. Integrating Node.js with ML models was also a hurdle, and we used Google Colab with TensorFlow for training. Our dedication led to MediLab's accuracy and reliability.

Accomplishments that we're proud of

We take pride in the precision and inclusivity of our diagnoses here at MediLab. Furthermore, our user-friendly website design reflects our commitment to accessibility, ensuring that all users can seamlessly access our platform across the globe and store their medical data in one place.

What we learned

During the development of MediLab, we gained invaluable insights into the realms of medical image processing and web development. We also came to appreciate the paramount significance of accuracy and reliability in medical diagnoses. Our major learning came in the form of integrating Vertex AI in our project for Blood Report Analysis.

What's next for MediLab

Looking ahead, MediLab has plans to broaden its scope by incorporating a wider range of medical reports and images. We are committed to an ongoing pursuit of heightened accuracy and reliability in our diagnoses. Moreover, we're dedicated to staying on the cutting edge of medical image processing, exploring new technologies and techniques to ensure that MediLab remains a leader in its field.