Improved Algorithm for the Detection of Cancerous Cells Using Discrete Wavelet Transformation of Genomic Sequences

Background: Cancer is the leading cause of mortality in worldwide. Cancer occurs due to anomalous mutations in a cell. Precise cancer diagnosis and specific course of treatment is essential for saving human lives. Objective: The main aim is to use digital signal processing techniques for the detection of cancer cells. Method: A method to classify the normal and the cancerous cells using discrete wavelet transformation has been developed. Here, the Deoxyribo nucleic acid sequences have been converted into numeric sequences using electron ion interaction potential values. Then wavelet transform is obtained. The cross correlation values of the wavelet coefficients of normal and cancerous cells have been calculated. The maximum cross correlation amplitude in transformed domain is calculated in order to detect the abnormality present in the nucleotides of the cells. Results: The test has been conducted on 82 cancerous Deoxyribo nucleic acid sequences and 82 normal Deoxyribo nucleic acid sequences. Standard performance metrics have been evaluated and the values obtained are sensitivity -98.78%, specificity -100%, accuracy -99.39%, Positive precision -98.78% and negative precision -100%. Conclusion: Comparing the performance metrics obtained with the methods in literature, it is found that the wavelet transformation method is better. Hence, this approach can be considered as an efficient solution for cancer detection. This method aids in early cancer detection and cancer therapeutics.