Economic considerations make the conventional chest radiograph (X-ray) film an important ingredient in the diagnostic process. An initial clinical investigation for patients with suspected lung ailments is the study of the chest X-rays. The problem of detection for diseases in their early stages are well known using X-ray. A technique involving wavelets coefficient as the feature vector and Andrew's Curve has been proposed for detection of Mycobacterium Tuberculosis (MTB). This paper presents new and important results whereby lung cancer (LC) may be detected and differentiated from MTB. A method to calculate misclassification probabilities is given.
A common practice in medical diagnosis and patient management is the comparison of two chest radiographs images. The difference between two digital images at two time points is a measure of the effect of treatment on the patient. Two measures of similarity, the ordinary regression coefficients, R(s)(2) and coefficients of determination for the Unreplicated linear functional relationship model (ULFR), R(f) (2), are used to compare images for the particular case of Mycobacterium Tuberculosis (MTB). Our results suggest that a series of R2 values indicates gradual declining trends with values falling within a band. New patients with a series of R2 values falling within this band may be consider as making a good or acceptable recovery.