Computer-aided tissue characterization for detection of thyroid cancer using multi-wavelength photoacoustic imaging.

Photoacoustic Imaging(PAI) is an emerging soft tissue imaging system that can be potentially used for the detection of thyroid cancer. Computer-Aided diagnosis tools help further enhance the detection process by assisting the radiologist in the elucidation of medical data. This study aimed to classify the malignant and non-malignant thyroid tissue using different machine learning algorithms applied to the multi-wavelength PA data obtained, generated by the excised thyroid specimens from actual thyroid cancer patients. An exhaustive comparative analysis among the performances of three machine learning algorithms, random forest, support vector machine, and artificial neural network was performed for classifying benign vs malignant thyroid as well as non-malignant vs malignant thyroid. While the random forest algorithm efficiently classified benign vs malignant thyroid with the highest accuracy than the other two algorithms, the support vector machine outperformed the other two algorithms in classifying non-malignant vs malignant with the highest specificity, the area under the receiver operating characteristics, and accuracy. This study shows that multiwavelength PA data can be used with suitable machine algorithms for efficient thyroid cancer detection.