Our Mission

DeepTumor is a comprehensive project aimed at revolutionizing the way brain tumors are diagnosed. The project consists of three distinct parts, each playing a crucial role in ensuring accurate and sustainable diagnosis.

Data Processing

Collecting and processing high-quality data images to train the deep learning model. It would involve to gather relevant patient data, ensuring the data is diverse and unbiased, and pre-processing the data to prepare it for deep learning.

Model Development

Developing and optimizing the deep learning model using TensorFlow. It would include selecting the most appropriate architecture, optimizing hyperparameters, and integrating advanced techniques data augmentation.

UI & Mail Integration

Developing a user-interface for users to interact with the model, as well as integrating the feature of sending output results via mail. It would involve developing features such as report generation and delivery via email or SMS.

The Outcomes

Increased Accuracy

DeepTumor has the potential to significantly increase the accuracy of brain tumor diagnosis.

Faster Diagnosis

DeepTumor has the potential to deliver accurate diagnosis reports to healthcare providers within seconds.

Improved patient outcomes

DeepTumor has the potential to improve patient outcomes and quality of life.

Sustainability

DeepTumor is designed with sustainability in mind, using high-quality data.

Increased accessibility

DeepTumor has the potential to increase accessibility to accurate brain tumor diagnosis.

Integration with existing healthcare systems

DeepTumor can be integrated with existing healthcare systems.

Potential for further research

The DeepTumor project can serve as a platform for further research into the diagnosis.

Economic benefits

DeepTumor has the potential to provide significant cost savings for healthcare providers and patients.

Let's get Tested!

DeepTumor is a brain tumor segmentation and classification project aimed at developing a deep learning model with over 1 million parameters using TensorFlow. Our model will be powered by fresh data images and optimized for accuracy and efficiency. We will employ green computing practices to ensure that our model contributes to Sustainability. One of the key features of DeepTumor is the development of a User Interface that enables easy interaction with the model. Users will be able to input patient details, and within seconds, the system will generate reports on tumor detection and classification. These reports will be delivered via email or SMS for user convenience.
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DeepTumor

In colab with @Tars.Ai