# DkFD: Optimizing Common Pediatric Dermatoses Detection with Novel Loss Function and Post-Processing
## 1 Introduction  
Pediatric dermatoses are a type of common illness that exhibit clinical traits such as short duration, rapid changes, unclear medical records, high contagiousness, easily caused complications, and children who are unable to correctly describe their symptoms [1]. Methods based on deep learning are the foundation of the majority of computer aided diagnosis (CAD) methods currently in use [2; 3; 4; 5; 6]. The Stanford team's 2017 work by [PERSON] et al. [7] is one of the most notable cases. It successfully demonstrated that the CNN classification model is able to classify keratinocyte carcinomas versus benign seborrheic keratoses and malignant melanomas versus benign nevi on the task of classification,which is comparable to professional dermatologists. It utilized the InceptionV3 network architecture, trained on a clinical dataset that has more than 2000 skin diseases. Encouraged by the results of this study, various types of neural-network-based medical photographic studies of dermatoses emerged [8]. Currently, most of the research in this field is focused on skin tumors and mostly on dermoscopy images, which are acquired by specialized equipment with simple backgrounds, based on the magnification of the lesion site, where the lesion area features are very clear. The International Skin Imaging Collaborative (ISIC) organizes the world's largest public repository of dermoscopy images and hosts skin image analysis challenges around the world. The results of this study inspired the development of numerous neural-network-based medical photographic studies of dermatoses. The most recent study in this area focuses on skin tumors and primarily uses dermoscopy images, which are obtained by specialized equipment with plain backgrounds when the lesion site is magnified, where the lesion area features are very distinct. The International Skin Imaging Collaborative (ISIC) conducts skin image analysis competitions and maintains the biggest public database of dermoscopy images in the world. In order to diagnose cutaneous lesions, [PERSON] et al. [9] integrated a number of cutting-edge CNN networks with Densenet, SENet, and ResNeXt, placing second in the ISIC Challenge. In 2019, [PERSON] et al. [10] addressing the problems of insufficient training data, inter-class similarity, intra-class variability, and lack of attention to the semantic lesion parts proposed an attention residual learning convolutional neural network (ARL-CNN) model for adaptively focusing on lesion regions of dermoscopic images. [PERSON] et al. [11] built the dermatology datasets Skin-10 and Skin-100 from Internet images, implemented an ensemble approach based on multiple CNN models, and presented an object-detection-based approach by introducing bounding boxes into the Skin-10 dataset in 2019. XiangyaDerm, a large dermatology clinical image dataset that is primarily from Asiatic, was proposed by [PERSON] et al. [12] in the same year. InceptionV3, Inception-ResNetV2, DenseNet, and Xception are four cutting-edge CNN models that have been chosen to show the classification performance of the CNN models and the applicability of XiangyaDerm as a benchmark dataset for dermatology diagnostics. Additionally, the necessity of creating distinct dermatological datasets for various areas and ethnicities was shown through cross-testing. [PERSON] et al. proposed an architecture of CNN to classify skin lesions, using a public dataset of 10,015 images consisting of seven types [13]. Since the studies on deep-learning-based classification and recognition in pediatric dermatology are relatively limited, in this paper, we first create a clinical images dataset named CPD-10 that contains 10 common pediatric dermatoses. All of its images are crawled from authoritative medical websites, including Hand-Food-And-Mouth Disease, Chickenpox, Mosquito Bites, Frununcle, Folliculitis, Atopic Dermatitis, Diaper Dermatitis, Impetigo, Urticaria, and Pyogenic paronychia.