# Eff-PCNet: An Efficient Pure CNN Network for Medical Image Classification
## 4 Experiments and Analysis
### Datasets  
(1) HAM10000 Dataset: HAM10000 [44] is a commonly used dermatologic image dataset for research in the field of machine learning and computer vision. This dataset collects a total of 10,015 dermatologic patient images for detecting pigmented skin lesions. In this paper, we randomly divide this dataset into a training set and a test set in the ratio of 8:2, where the training set is 8015 images and the validation set is 2000 images. It mainly includes seven different types of skin cancers: melanoma (MEL, 11.1%), melanocytic nevus (NV, 66.9%), basal cell carcinoma (BCC, 5.1%), actinic keratosis (AKIEC, 3.3%), benign keratoses (BKL, 11%), dermatofibromas (DF, 1.1%), and vascular lesions (VASC, 1.4%). The data distribution of the HAM10000 dataset is shown in Table 1, and the samples for each category are shown at the first column of Figure 6.  
(2) SkinCancer Dataset: SkinCancer is a mini-dataset of the ISIC 2018 [45] challenge dataset. Due to the imbalance of samples in the categories in [45], this dataset removes dermoscopic images of diseases other than melanoma from the original data and retains only benign skin moles and malignant skin moles, the two categories with a more balanced number of samples. The dataset has a total of 3297 samples. We maintain a consistent division with [46]. The training set has a total of 2637 samples containing 1440 benign samples and 1197 malignant samples. The test set has a total of 660 samples containing 360 benign samples and 300 malignant samples. The distribution of lesions on the small-ISIC2018 dataset is shown in Table 2, and an example of malignant skin moles image is shown in the second column of Figure 6.  
\begin{table}
\begin{tabular}{c c c c c c c c c} \hline \hline
**Dataset** & **MEL** & **NV** & **BCC** & **AKIEC** & **BKL** & **DF** & **VASC** & **Tottal** \\ \hline Train & 891 & 5364 & 421 & 262 & 880 & 92 & 114 & 8015 \\ Test & 222 & 1341 & 102 & 65 & 219 & 23 & 28 & 2000 \\ Total & 1113 & 6705 & 514 & 327 & 1099 & 115 & 142 & 10,015 \\ \hline \hline \end{tabular}
\end{table}
Table 1: Distribution of lesions in the HAM10000 dataset.  
(3) Chest-Xray Dataset: The Chest-Xray dataset was developed by [47] published online, the chest-Xray images in the dataset were selected from pediatric patients between the ages of 1 and 5 years old at the Guangzhou Women's and Children's Medical Center (GWCMC), and a total of 5856 high-resolution chest-Xray images were collected. The dataset was divided into two categories, normal and pneumonia, with 1583 samples of normal images and 4273 samples of pneumonia images. We randomly selected 1341 samples of normal images and 3875 samples of pneumonia images as the training set, and 242 samples of normal images and 398 samples of pneumonia images as the test set for the experiments.The distribution of the data in the Chest-Xray dataset is shown in Table 3, and the example of pneumonia is shown in thethird column of Figure 6.  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Dataset Split** & **Benign** & **Malignant** & **Total** \\ \hline Train & 1440 & 1197 & 2637 \\ Test & 360 & 300 & 660 \\ Total & 1800 & 1497 & 3297 \\ \hline \hline \end{tabular}
\end{table}
Table 2: Distribution of lesions in the SkinCancer dataset.  
Figure 6: Original images on the three datasets.