Automatic Detection and Classification System for Mesothelioma Cancer Using Deep Learning Models with HPO

Mesothelioma is a deadly cancer, but its early detection is important to save the life of a human. Hence, the paper focuses on the development of a novel method to detect Mesothelioma cancer using deep learning techniques like Gated Recurrent Unit, Multilayer Perceptron, and Long Short-Term Memory along with GridSearchCV(a hyper-parameter optimization technique). To evaluate the method, an experiment has been conducted on the dataset of 324 records, where 228 represent healthy individuals and 96 depict Mesothelioma patients. After analyzing and studying its pattern, feature selection technique such as Standard Scaler is applied to remove extraneous attributes. Besides this, SMOTE technique has been also used to address class imbalance and balance the binary classes in the data. During model training, all the applied models have been trained as well as examined for the parameters like precision, accuracy, loss, F1-score, recall, and AUC-ROC. In addition to this, for enhancing the performance of MLP model,  GridSearchCV has been incorporated to fine-tune the hyper-parameters. During experimentation, the results show that the MLP model incorporated with GridSearchCV optimizer achieves the highest testing accuracy of 98.97%, precision and AUC-ROC of 1.00, while as F1-score and recall of 0.98. These findings indicate that our proposed approach obtained through  GridSearchCV demonstrates improved performance and serves as a reliable tool for early Mesothelioma detection.