Classification and Prediction of Pleuro Pulmonary Blastoma Using Deep Learning Models
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Keywords:
Pleuro Pulmonary Blastoma, Machine Learning, Classification, ResNet-based Convolutional Neural Network, Long Short-Term MemoryAbstract
Pleuro-pulmonary Blastoma (PPB) is an uncommon childhood illness caused by embryonic malignancies that have aberrant tissue growth on the pleural surfaces and in the lung parenchyma. Tumors can develop by several methods, allowing them to be categorized into categories I, II, and III. Each type is associated with distinct variances in the age at which they are diagnosed and the prognosis. Our goal was to provide a comprehensive examination of the relevant literature, outlining the features of these tumors and the use of multidisciplinary approaches to treat them, with a specific emphasis on surgical intervention. As described in prior writings, pathologists use patient samples such as MRI scan images, DNA sequencing, etc., to predict and categorize PPB variations. Various machine learning methods are utilized to extract features from proteins according to their protein family, but in the suggested methodology, we used various deep learning models to automatically extract features and for classification. We considered using Deep Learning algorithms, such as ResNet-based Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM), by taking into consideration protein sequence as input data to obtain accurate results and perform the prognosis of type I, II, and III. We discover the connection between the functional annotations of unaligned amino acid sequences and these deep learning models.
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