Focal cortical dysplasia (FCD) is a malformation of cortical development, which was often characterized by cortical thickening and blurring of the white/gray matter junctions on magnetic resonance (MR) imaging . FCD is most observed in patients who have drug-resistant epilepsy and are candidates for surgery . With technological advancements in MRI, FCD diagnosis has been revolutionized, and epilepsy surgery seems to be more successful by the better detection of the lesion locations. However, many cases are still being reported to have normal MRI despite the malformation . Therefore, finding new diagnosis methods with better performance than human interpretations is needed.
Recently, computer-aided diagnosis (CAD) systems have attracted much attention from medical imaging and diagnostic radiology researchers. This technology aims to interpret medical images faster and without dependence on the experience of radiologists in diagnosing brain disorders. The CAD system facilitates the processing of medical images using pattern recognition techniques .
In a CAD system, several steps are performed: First, the MRI image is provided as the input for the CAD system and as training samples. Then, preprocessing is performed to remove the samples not related to the diagnosis. In the segmentation step, images are grouped into regions. Feature extraction on images is performed after that. Finally, based on the extracted feature matrix and subject labels, two or more classes are defined in the classification stage.
In recent years, researchers have developed various machine learning systems to study epilepsy. El Azami et al.  developed a machine learning system based on SVM classification to detect epileptogenic abnormalities in patients who have epilepsy. They applied the system to 11 patients with 13 FCD lesions (3 MRI-positive and 10 MRI-negative) and 77 healthy subjects. The SVM classifier detected all lesions in patients with positive MRI, while 7 out of 10 patients with negative MRI were correctly diagnosed.
Gill et al.  proposed an automated FCD lesions detection algorithm using surface-based morphometric features extracting from T1 images. For each image, morphological, intensity, and gradient features were extracted at both cortical and subcortical surfaces. Using 5-fold cross-validation, the decision tree classification was performed on 41 patients with FCD lesions and 38 healthy subjects. The results showed a sensitivity of 83% and a specificity of 93%.
Besson et al.  proposed a method for identifying FCD lesions on T1-weighted MRI images based on surface features including cortical thickness, curvature, inner cortical surface depth, gradient magnitude at the white matter / gray matter interface, and cortical signal intensity. Automatic detection was performed by the neural network. This method was tested on 19 patients with FCD and was detected in 89% of cases.
In a study conducted by Adler et al. , surface-based morphometry and neural network methods were used to identify FCD lesions in a group of children. The neural network classification was trained using data from 22 patients with FCD lesions and 28 healthy subjects. FCD was identified in this group of children with a sensitivity of 73%.
Also, Jin et al.  employed the methods used in Adler's study for many patients with confirmed FCD lesions, including children and adults. The results showed a sensitivity of 73.7% and a specificity of 90%.
In this study, we aimed to find the best method for classifying images of patients with FCD lesions from normal images. In the first step, we obtained structural images, preprocessed and processed the images using Freesurfer software, and extracted surface-based features from each Desikan-Killiany Atlas region  (34 per hemisphere). Then, we classified the images using the common classifier and compared the results.