The Data
We used a large dataset consisting of brain MRI scans, sourced from medical research
institutions specializing in neurology and oncology. Each MRI scan in the dataset was meticulously
labeled by human experts, indicating whether it contained a specific type of brain tumor or no tumor at
all.
The MRI scans provided were high-resolution images, offering detailed views of the brain
structures. As each of the images was already labeled by medical experts, we only needed to sort
the data into specified categories, or target classes, and start our preprocessing steps.
Our
dataset
did not include any patient information, as we felt that it was beyond the scope of our goal with this
particular model.
Before Training
Before we could train, we had to first increase the number of samples, so we used data augmentation to increase the number of images in our dataset so that our model could better understand the data, and find patterns within the images. This would help it to "learn" better representations of how certain tumors appear on the scans.
Training
With our data ready and sort-of balanced, we proceeded to train our deep learning model. We used a
convolutional neural network (CNN) architecture, known for its efficacy in image recognition
tasks.
The
training process involved the following steps:
- 1. Splitting the Data:
We divided the dataset into training, validation, and test sets. The training set was used to teach the model, the validation set to tune hyperparameters, and the test set to evaluate final performance.
- 2. Training the Model:
Our model was trained on the labeled MRI images, learning to extract features indicative of the presence or absence of tumors. This involved multiple epochs of training, with the model iteratively improving its accuracy through backpropagation and optimization techniques.
- 3. Evaluating the Model:
After training, we tested the model on the test set, yet again, consisting of images it had not seen before. Through our testing, the model could accurately identify tumors in unseen MRI scans, and also detect if there was no tumor in the MRI scans. We compared the model's outputs, to the actual labels given by human experts, and it was a match.
The Final Phase
Our trained vision model proved to be extremely accurate in predicting whether a given brain MRI scan contains a specific
type of tumor or is tumor-free. This innovation holds significant promise for the medical field,
particularly for radiologists and oncologists.
Here are some advantages:
- Rapid Screening:
The model can quickly analyze MRI scans, providing preliminary assessments that can speed up the diagnostic process drastically. - Increased Accuracy:
By assisting human experts, the model can help reduce diagnostic errors, ensuring more reliable identification of brain tumors. - Resource Efficiency:
Automating the initial screening process can save valuable time and resources, allowing medical professionals to focus on detailed analysis and treatment planning for positive cases.
The integration of AI with medical imaging holds so much hope. It has the potential to transform the early detection and diagnosis of brain tumors, breast cancer, and all kinds of other diseases. Our model only shows how technology can augment human expertise, leading to improved patient outcomes and advancing the field of medical research.