DEEP LEARNING BASED BiLSTM ARCHITECTURE FOR LUNG CANCER CLASSIFICATION

Currently, health related issues are the most crucial and life-threatening issues for humans. These health issues include several chronic diseases such as diabetes, cardiovascular disease, respiratory disease, cancer and many more. At present time, cancer is considered as one of the most deadly disease which is increasing consistently in both men and women. Cancer is a medical condition which is caused due to uncontrolled growth of cells present in the lung. The early prediction of its symptoms can help doctors to diagnose it efficiently. Hence, automated computer aided design systems are proposed to identity the lung cancer. These systems are based on the machine learning process where initially, database is learned through their specific process of learning and then classification process is applied. However, these systems fail to deliver the desired performance for medical applications. To overcome this issue, we focus on the deep learning technique and introduced Long-Short Term Memory (LSTM) based model which flows the information in both direction i.e. forward to backward and backward to forward for better learning. This scheme of LSTM is known as BiLSTM because it process the data in two directions. Prior to this, the data preprocessing technique is also incorporated which performs missing value imputation using KNN imputation. The proposed approach is implanted using MATLAB tool and tested on Lung Cancer UCI dataset. The performance is measured in terms of classification accuracy, precision, recall, specificity, sensitivity and F1-score. Further, we compare the performance with existing techniques. The comparative analysis shows that proposed approach achieves better performance when compared with state-of-art techniques.