Cancer bears a poisoning threat to human society. Melanoma, the skin cancer, 
originates from skin layers and penetrates deep into subcutaneous layers. There 
exists an extensive research in melanoma diagnosis using dermatoscopic images 
captured through a dermatoscope. While designing a diagnostic model for general 
handheld imaging systems is an emerging trend, this article proposes a 
computer-aided decision support system for macro images captured by a 
general-purpose camera. General imaging conditions are adversely affected by 
nonuniform illumination, which further affects the extraction of relevant 
information. To mitigate it, we process an image to define a smooth illumination 
surface using the multistage illumination compensation approach, and the 
infected region is extracted using the proposed multimode segmentation method. 
The lesion information is numerated as a feature set comprising geometry, 
photometry, border series, and texture measures. The redundancy in feature set 
is reduced using information theory methods, and a classification boundary is 
modeled to distinguish benign and malignant samples using support vector 
machine, random forest, neural network, and fast discriminative 
mixed-membership-based naive Bayesian classifiers. Moreover, the experimental 
outcome is supported by hypothesis testing and boxplot representation for 
classification losses. The simulation results prove the significance of the 
proposed model that shows an improved performance as compared with competing 
arts.