"MVPO Predictor: "Deep Learning for Brain Tumor Classification and Survival Prediction with MRI"

What is it about?

Brain tumor is a severe nervous disorder that causes damage to health and often leads to death. Therefore, it is significant to classify the brain tumor at an early stage as it increases the survival rate of patients. One of the commonly employed imaging modalities for brain tumor classification is Magnetic Resonance Imaging (MRI). However, it is relatively complex to perform the brain tumor classification process due to the variations of type, shape, size and tumor location. To overcome such issues and classify the tumor more accurately, a deep learning classifier named Deep Maxout network is developed to classify the tumor into different grades. Based on the classification result, the features connected with the tumor grades are effectively acquired to make the survival prediction process. Deep learning is an effective and robust classifier model employed to perform the tumor classification or detection process with the MRI modality. Here, the survival prediction of tumor patients is carried out by the Deep Long Short-Term Memory (LSTM) classifier. Accordingly, the proposed method achieved higher performance using accuracy, sensitivity, specificity and prediction error with the values of 0.9434, 0.9324, 0.9202 and 0.0579.

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Why is it important?

Early classification of brain tumors significantly increases the survival rates of patients by enabling timely intervention and treatment. Identifying tumors at an early stage allows for more effective management and potentially life-saving interventions. Brain tumors exhibit variations in type, shape, size, and location, making their classification using traditional methods relatively complex. Employing advanced techniques, such as deep learning, can overcome these challenges and improve the accuracy of tumor classification.

Perspectives

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Deep learning, particularly the Deep Maxout network, is employed as a robust classifier for accurately classifying brain tumors into different grades. Deep learning models excel in capturing complex patterns and relationships within data, making them well-suited for tasks such as tumor classification. The deep learning classifier extracts relevant features associated with tumor grades, enhancing the accuracy of classification. By learning hierarchical representations of the input data, deep learning models can effectively differentiate between tumor grades based on subtle variations in MRI images.

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