## 3 Implementation and Experimentation
### Dataset  
The dataset used in this study is from the Cancer Imaging Archive (TCIA) [32,33]. The data were originally annotated and labeled by experts from Thomas Jefferson University and Henry Ford Hospitals. This dataset is composed of MR scans for 130 subjects belonging to three classes including low-grade, high-grade and healthy subjects. This public dataset is one of the most trusted online datasets; however, it has a few limitations. Firstly, the dataset is composed of MR scans from 130 subjects. However, the ground truths were only provided for 126 subjects of the total number of subjects available in the dataset. Secondly, the ground truths provided were for the entire MR scan, which implies that the information at the slice level is missing. Finally, the segmentation information was not provided, thus, the labels at the pixel level were not given. Neurologists have done a two-phase examination. The first phase consists of verifying all the annotations given by Thomas Jefferson University and Henry Ford Hospitals' experts by acting as second observers. Furthermore, they labeled the 4 unlabeled MR scans. It took approximately 2-3 min to examine each scan. This time includes the time required for loading, analyzing and providing feedback. The second phase of reviewing these scans was focused on the slice-level details. In this report, we used 2D ConvNet for the training and testing of the Computer-Aided Diagnostics (CAD) tool. Not all the slices of a full brain MRI scan of a patient with glioma tumor would show the lesion. Thus, neurologists selected only those few slices from each scan of the low-grade and high-grade tumor class that contained the lesion. The neurologists went through each scan, selecting an average of 31 slices from each MRI scan. The whole process was performed very carefully making sure that the selected slices from the low-grade and high-grade glioma class did not include any healthy slices. In this phase, It took approximately 5-7 min to analyze each scan. In our dataset, the low-grade class consisted of Astrocytoma II and Oligodendroglioma II. The high-grade dataset class was a combination of GBM, Astrocytoma III and Oligodendroglioma III. The third and final class of the dataset was comprised of healthy subjects. These image archives from 130 subjects resulted in 4069 2D image samples in total. On average, 31 2D slices were selected out of each brain MR scan. The entire data break down of low-grade and high-grade gliomas is shown in the Table 1. Additionally, the overall data segregation with respect to gender, age and race among low-grade and high-grade glioma patients is shown in Table 2.  
It is very important to diagnose glioma in the early stage for the greater prognosis of patients. However, one of the limitations of existing CAD schemes is that they perform well only on the detection  
Figure 3: Simplified network architecture. of large-sized tumors. This reemphasizes the fact that it is significantly important to take into account the size and the shape of tumors. In Table 3, the range of tumor size measurements within this patient group is displayed. The lesion size was calculated by using the largest perpendicular (x-y) cross-sectional diameter of signal abnormality (longest dimension \(\times\) perpendicular dimension) measured on a single axial image only. The 1D measurements were provided by radiologists for 99 subjects. The 2D size of the lesion area is a computational approximation by multiplying the 1D cross-sectional diameters. Figure 4 shows the size of lesion for each of the individual patients along both major perpendicular axes. Some of the patients in this study have a lesion size less than 2 cm. All images available from the database are used for training and evaluation of the designed classifier irrespective of the lesion sizes. Additional details regarding the detailed distribution of lesion size among the 18 classes are provided in Appendix B.