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.