Diabetic foot is among the major complications of Diabetes Mellitus (DM) that can results in ulcerations. An early detection and appropriate treatment can prevent traumatic outcomes such as lower extremity amputation. Furthermore, studies have shown that temperature fluctuations on the plantar foot can be related to diabetic foot complications. Since the time of Hippocrates, human body temperature is being associated to health. Thermography is a non-invasive imaging method employed to view the thermal patterns using Infrared (IR) camera. In addition, it allows the qualitative and visual documentation of temperature fluctuation in vascular tissues. Absolutely, IR thermography can play a crucial role in the medical field, especially for detection of diabetic foot disease. Nonetheless, such strategy is yet to be established and many circumstances still unsolved. The first challenge is analysing the thermal changes because the risks of ulceration are associated to the rise in plantar temperatures. Nevertheless, the attempts to identify the spatial patterns of temperature remains difficult because there are extensive forms of thermal patterns. This complicates the classification process. In addition, the interpretation of thermographic patterns may be difficult due to inadequate details, even in healthy subjects, and classification techniques on the thermographic patterns. Moreover, there is no objective approach in allocating the thermogram to certain classes. Recent studies proposed the temperatures of one foot and contralateral foot to be measured and compared, and the diabetic foot risk regions are detected by the defined threshold. However, this is only applicable in without ulceration or amputation cases and in asymmetric thermal changes. III Therefore, this research developed a novel computer aided detection (CAD) system for diabetic foot using nonlinear features extracted from plantar foot thermograms. A total of 33 healthy volunteers and 33 non-neuropathy diabetic patients are recruited from Ngee Ann Polytechnic (NPIRB-P0175-2017-ECE-AMA6) and Singapore General Hospital (SGH), Diabetes & Metabolism Center (DMC) (CIRB Ref: 2016/3044) respectively. The thermograms acquired are pre-processed by segmenting the plantar foot regions using polygon and then proceed to warp all the segmented plantar foot regions into uniform size. Afterward, the warped grayscale foot images are decomposed using Discrete Wavelet Transform (DWT) and Higher Order Spectra (HOS) prior to extracting texture and entropy features. The features values from left and right foot are subtracted. Subsequently, student t-test is applied on the resultant features to select and rank the significant features. Lastly, the 27 significantly ranked features (p value < 0.0001) are fed independently into support vector machine (SVM) classifier. The developed methodology achieved maximum classification accuracy of 89.39%, positive predictive value of 96.43%, sensitivity of 81.81% and specificity of 96.97% using only five features. The findings of this researc...