Brain Tumor Classification

Early classification and diagnosis of Brain Tumors are essential for providing the right treatment to a patient. It is crucial to get treatment as soon as possible because the survival rate for someone with an untreated brain tumor can range from as low as 3 months to as high as 5 years. In this project, we classified brain tumor images into 4 categories: glioma, meningioma, pituitary, and no tumor. With the use of baseline and deep learning models, the deep learning models demonstrated a significantly higher performance due to their ability to analyze images. The model with the highest accuracy was the MobileNet, a pre-trained transfer learning model trained on 5,608 images. This model yielded a validation accuracy of 98.24%. Using metrics including Kappa cohen score, precision, and recall, we validated the machine learning model's performance. We deployed the MobileNet model to a web app using Streamlit, where users submit MRI images and receive diagnoses of tumor class. We found that the model performed very well while utilizing the web app, indicating that it is safe to be used. However, since we only have 4 classes and there are over 150 total types of brain tumors, it could easily get a diagnosis wrong if it is not in one of these 4 classes.

Early classification and diagnosis of Brain Tumors is essential for providing the right treatment to a patient. It is crucial to get treatment as soon as possible because the survival rate for someone with an untreated brain tumor can range from as low as 3 months to as high as 5 years, which is why it is extremely important to get treatment as soon as possible. In this project, we used brain tumor MRI images to classify them into these 4 different categories: glioma, meningioma, pituitary, and no tumor. With the use of baseline and deep learning models, the deep learning models demonstrated a significantly higher performance due to their ability to analyze images. The model with the highest accuracy was the MobileNet model, a pre-trained transfer learning model trained on 5,608 images. This model yielded a validation accuracy of 98.24%. Using metrics including Kappa cohen score, precision, and recall, we validated the performance of the machine learning model. We deployed the MobileNet model to a web app using Streamlit, where users submit MRI images and receive diagnoses of tumor class. We found that the model performed very well while utilizing the web app, indicating that it is safe to be used. However, since we only have 4 classes and there are over 150 total types of brain tumors, it could easily get a diagnosis wrong if it is not in one of these 4 classes.

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Comparing Machine Learning Models to Determine Which is Most Effective at Detecting Brain Tumors

By using machine learning techniques to analyze various brain tumor scans, the goal was to determine which techniques are the most efficient and accurate in determining the presence of brain tumors.

Samya C.

Mentored by Shreya Parchure

Classification of Exon and Intron Boundaries

The goal was to accurately classify exon and intron boundaries based on DNA sequences. Scientists can learn more about proteins if divisions between exons and introns are clear. We used multiple different machine learning approaches including a logistic regression model, a multilayer perceptron, a LSTM, and a model that included a LSTM, autoencoder and a multilayer perceptron. LSTMs performed well, pointing to the idea that order of nucleotides is important when classifying DNA sequences.

Milo B.

Mentored by Samuel Kwong

Multi-Label Prediction of Protein Subcellular Localizations through Machine Learning and State-of-the-art Structural Embeddings

This research explores various machine learning models to predict protein subcellular localization using UniProt’s Swiss-Prot database and Meta’s high-performing ESM2 structural embeddings.

Alfred B.

Mentored by Ayush Pandit

Brain Tumor Classification

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Research Paper