NeuroAdvisor

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  Tumor Detection

Inspiration

Brain tumors are one of the most serious and life-threatening medical conditions, and early detection plays a crucial role in improving patient survival rates. However, manual analysis of MRI images by radiologists can be time-consuming and requires high expertise. We were inspired to develop NeuroAdvisor, an AI-powered tumor detection system that can assist in identifying tumors quickly and accurately. Our goal was to leverage Deep Learning to support healthcare professionals and contribute to faster and more reliable diagnosis.

What it does

NeuroAdvisor is an intelligent tumor detection system that analyzes MRI scan images and predicts whether a tumor is present or not. The system: Uses MRI image datasets Detects the presence of tumors automatically Classifies images as Tumor or No Tumor Provides quick and accurate predictions Users can upload a new MRI image, and the system will instantly display the prediction result based on the trained model.

How we built it

We built NeuroAdvisor using Convolutional Neural Networks (CNN), which are highly effective for image classification tasks. Our process included: Collected brain tumor MRI datasets from Kaggle The dataset contained different types of tumor images and non-tumor images Split the dataset into training and testing data Preprocessed images by resizing and normalization Built a CNN model to learn image features automatically Trained the model using the training dataset Evaluated performance using the test dataset Finally, created a system where users can upload test images to detect tumors Tools & Technologies used: Python TensorFlow / Keras CNN Kaggle Dataset NumPy, Matplotlib

Challenges we ran into

During the development of NeuroAdvisor, we faced several challenges: Understanding how CNN works for medical image classification Handling large image datasets Improving model accuracy Avoiding overfitting Proper preprocessing of images Training the model efficiently Medical image classification requires careful tuning and experimentation.

Accomplishments that we're proud of

We are proud that we successfully: Built a working CNN-based tumor detection model Trained the model using real MRI datasets Achieved accurate prediction results Created a system that can detect tumors from uploaded images Applied AI in a real-world healthcare problem This project helped us understand how AI can contribute to saving lives.

What we learned

Through this project, we learned: How Convolutional Neural Networks work Image preprocessing techniques Model training and testing Deep Learning in healthcare applications Importance of data quality Practical implementation of AI models We also improved our skills in Python, Machine Learning, and problem-solving.

What's next for NeuroAdvisor

In the future, we plan to improve NeuroAdvisor by: Increasing dataset size for better accuracy Detecting different types of tumors separately Building a web application interface Providing confidence score Deploying the model for real-world use Integrating it into hospital systems Our vision is to make NeuroAdvisor a reliable assistant for medical diagnosis.

Built With

• python-tensorflow-/-keras-cnn-kaggle-dataset-numpy