Using Deep Ensemble Learning & Deep CNN For Cancer Prediction

Artificial Intelligence & Deep Learning

Cancer is one of the dangerous diseases known to mankind. There are thousands of women who are affected by Cervical Cancer. Here we focus on classification of Lungs & cervical cancer cells .We aim here to minimize analytical errors while diagnosing the cancer .The project has many different types of cell classes .This cell are classified into cancer categories. The classification of Pap smear images to detect cervical dysplasia. This classification is to be done with deep learning methods. The main purpose here is to get better result and accuracy while detecting cervical dysplasia. Deep Neural Network with Convolutional Neural Network is used to classify the cells into different categories. Models are trained and tested with different parameters for better accuracy. Then they are ensembled with averaging of models. The models were able to produce pretty good results. The accuracy of predicted and true labels were high. This makes sense that using this methods to reduce the human errors will be significantly high and efficient. The models were able to produce accuracy above 80%, with ensemble it was higher than single models. This tells that if single models are ensembled with other different models then they can perform pretty good and then they can have much more higher accuracy than the single models. The project executed on CPU+GPU gives higher result than execution on CPU. The RAM of machine also plays important role in Time Complexity of model. GPU increase the performance of model and same time reduces the execution time. For this project Kaggle Online Cloud Library is used. Early detection of the cancer is an enormous challenge. Analysis and cure of lung malignancy have been one of the greatest difficulties faced by humans over the most recent couple of decades. For Lung Cancer, early identification of tumor would facilitate in sparing a huge number of lives over the globe consistently. This paper presents an approach which utilizes a Convolutional Neural Network (CNN) to classify the tumors found in lung as malignant or benign. The accuracy obtained by means of CNN is 96%, which is more efficient when compared to accuracy obtained by the traditional neural network systems.

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