Facial Skin Type Analysis Using Few-shot Learning with Prototypical Networks

Facial skin type analysis is a critical task in several fields, including dermatology, cosmetics, and biometrics, and has been the subject of significant research in recent years. Traditional facial skin type analysis approaches rely on large, labeled datasets, which can be time-consuming and costly to collect. This study proposes a novel few-shot learning (FSL) approach for facial skin type analysis that can accurately classify skin types with limited labeled data. A diverse dataset of facial images with varying skin tones and conditions was curated. The proposed approach leverages pre-trained deep neural networks and an FSL algorithm based on prototypical networks (PNs) and matching networks (MNs) to address the challenge of limited labeled data. Importantly, this study has significant implications for improving access to dermatological care, especially in underserved populations, as many individuals are unaware of their skin type, which can lead to ineffective or even harmful skincare practices. Our approach can help individuals quickly determine their skin type and develop a personalized skincare routine based on their unique skin characteristics. The results of our experiments demonstrate the effectiveness of the proposed approach. PNs achieved the highest accuracy in the 2-way, 10-shot, 15-query scenario with an accuracy of 95.78 ± 2.79%, while MNs achieved the highest accuracy of 90.33 ± 4.10% in the 2-way, 5-shot, 10-query scenario. In conclusion, this study highlights the potential of FSL and deep neural networks to overcome the limitations of traditional approaches to facial skin analysis, offering a promising avenue for future research in this field.

Skin is the outermost organ of the body that covers the human body. The skin makes up 15% of the total body weight. On the outer surface of the skin there are pores (cavities) where sweat is released. The skin has many functions, including as body armor, as a sense of touch or communication tool, and as a temperature regulator. Most people, especially women, have white, healthy, clean and well- groomed facial skin. Cosmetics Distributor is a line of business that sells cosmetic products. In identifying the consumer's facial skin type, it is carried out by employees who are not experts. Often there is an error asking the type of skin and to hire a doctor or expert requires a large amount of money. The problems in using products that do not pay attention to skin type or do not know it, causing new problems such as acne, dry skin and others. These problems can be solved by the field of science in detecting a person's facial skin based on expert knowledge, then the science is an Expert System using Certainty factor. The results of this study can identify the type of facial skin based on the existing symptoms, it is hoped that it can help distributors and detect skin quickly and accurately.

In this paper, we equip Prototypical Networks (PNs) with a novel dissimilarity measure to enable discriminative feature normalization for few-shot learning. The embedding onto the hypersphere requires no direct normalization and is easy to optimize. Our theoretical analysis shows that the proposed dissimilarity measure, denoted the Squared root of the Euclidean distance and the Norm distance (SEN), forces embedding points to be attracted to its correct prototype, while being repelled from all other prototypes, keeping the norm of all points the same. The resulting SEN PN outperforms the regular PN with a considerable margin, with no additional parameters as well as with negligible computational overhead.

Skin is part of the human body that has a function as a barrier from the external environment and gives a physical appearance to an individual. In general, human skin types are classified into normal, dry, oily and combination skin. These skin types affected by the amount and change in facial sebum secretion and hydration ability. Determination of the type of facial skin is needed to determine skin care products and cosmetics in accordance with the type of facial skin they have. In this research, a system design for digital-based facial skin types using Convolutional Neural Network (CNN) method which has advantages to produce features and characteristics from the microscopic images dataset. The primary datasets taken directly using microscopic cameras and have been validated by a dermatologist. The CNN proposed model in this study consists of 3 hidden layers that use 3 × 3 size filters with output channels 8, 16 and 32 respectively, fully connected layer and softmax activation. The proposed model was able to classify the skin types into normal, dry, oily skin conditions and the combination with the best accuracy of 99.5% from 1200 training images and 400 test images used, meanwhile the parameters of recall precision and f-1 score produce values close to 1, which means that it is almost perfect or it can be said that the error is small.

Accurate identification of pathogenic bacteria from images taken under light microscopy is an important role in clinical diagnosis and microbiology. Traditional deep learning methods such as Convolutional Neural Networks (CNNs) have achieved excellent performance but are based on enormous, well-annotated datasets that may not be readily available. Absence of labeled images of bacteria is one of the key challenges in real-world applications, where class imbalance and diversity of samples affect generalization. Recent years have witnessed few-shot learning being a potential paradigm for mitigating data paucity by enabling models to learn well from sparse samples. Among numerous few-shot approaches, prototypical networks have proven useful for image classification with sparse data. This paper overviews the progress in bacterial identification from light microscopy images, reviews existing classical and deep learning methods, and highlights how prototypical networks solve the shortcomings of conventional models through few-shot learning. The review concludes with open problems and future directions, including integrating fewshot learning into web-based, scalable diagnostic platforms for efficient pathogen detection.

This research aims to develop a Facial Skin Type Classification System using Convolutional Neural Networks (CNN) with the MobileNetV2 architecture implemented on Android-based smartphone devices. Facial skin is classified into four main categories: normal, combination, oily, and dry, based on characteristics such as moisture, oil production, and skin texture. CNN was chosen for its ability to accurately recognize image patterns, while MobileNetV2 is designed for low-spec devices, allowing the classification process to run quickly and efficiently. The deep learning method is used to classify facial skin types with optimal accuracy and computational efficiency. The facial skin image dataset was collected and trained using transfer learning on the MobileNetV2 model. The research process includes the collection of facial skin image datasets, preprocessing, model training, testing, and implementation in the application. The facial skin image dataset was collected and trained using transfer learning on the MobileNetV2 model. The results show that this system is capable of classifying skin types with high accuracy of 87.5% in an average computation time of 0.4 seconds at a distance of 15–20 cm under bright lighting conditions

Our generation has seen the boom and ubiquitous advent of Internet connectivity. Adversaries have been exploiting this omnipresent connectivity as an opportunity to launch cyber attacks. As a consequence, researchers around the globe devoted a big attention to data mining and machine learning with emphasis on improving the accuracy of intrusion detection system (IDS). In this paper, we present a few-shot deep learning approach for improved intrusion detection. We first trained a deep convolutional neural network (CNN) for intrusion detection. We then extracted outputs from different layers in the deep CNN and implemented a linear support vector machine (SVM) and 1-nearest neighbor (1-NN) classifier for few-shot intrusion detection. few-shot learning is a recently developed strategy to handle situation where training samples for a certain class are limited. We applied our proposed method to the two well-known datasets simulating intrusion in a military network: KDD 99 and NSL-KDD. These datasets are imbalanced, and some classes have much less training samples than others. Experimental results show that the proposed method achieved better performances than the state-of-the-art on those two datasets.

Background/alms: Worldwide there are many skin care and makeup products intended to maintain good appearance or youthful skin. However consumers don't know which products are suitable for their skin because they don't know their skin type exactly. To solve this problem, this study suggests two possible classifications of facial skin type by using simple methods.Methods: In 662 healthy volunteers, sebum excretion rate (SER) on the forehead and cheek, and skin surface patterns on the cheek were examined by using Sebutape® and skin replica, respectively.Results: The measured SER values from the forehead were 0.06‐4.56 ng/cm2/min and those from the cheek were 0.04‐3.80 ng/cm2/min. From these data, five facial skin types were classified by SER: low SER type, medium SER type, high SER type, combination‐1 SER type, and combination‐2 SER type. Twelve facial skin types were also classified; they were determined by star formation (SF), primary lines (PL), secondary lines (SL) and pore size from magnified skin surface relief (SSR) of the cheek.Conclusions: From this study, we suggest new classifications of skin types by SER and SSR. The SER and the parameters of skin surface texture (SF, PL, and SL) decreased with age and the pore size increased with age. Although these methods did not classify facial skin types perfectly, we were able to check consumer's facial skin types simply and more quickly.

IntroductionThe first and one of the most important steps in facial plastic surgery is accurate preoperative facial analysis and recording of data that may help the surgeon to check the outcomes of his/her techniques, promoting a surgeon's professional development.ObjectiveTo evaluate the esthetic outcomes of external septorhinoplasty relevant to ethnic facial harmony and to investigate the relationship of the columellar incision scar with the type of skin and columellar incision type in a Turkish population.MethodsIn total, 28 consecutive adult male patients with a mean age of 32.14 ± 10.66 years (range: 18–61 years) were included the study. Primary outcomes were preoperative and postoperative photogrammetric facial analyses of the patients including measurement of nasofrontal angle, nasolabial angle and nasal projection ratios (Gode) assessed according to the data derived from the Rhinobase program. Results were compared to facial proportions of the Turkish population. Columellar incision scar scores related to the Fitzpatrick skin type classification of the patients and columellar incision types used for the external approach were secondary outcomes of the study.ResultsMean preoperative and postoperative nasofrontal angles were 148.04° ± 8.18° and 144.50° ± 7.15°, respectively, while mean preoperative and postoperative nasolabial angles were 87.59° ± 14.01° and 98.50° ± 9.71°, respectively. Mean preoperative and postoperative nasal tip projection ratios were 0.56 ± 0.05 and 0.60 ± 0.06, respectively. The differences between pre- and postoperative measurements were all significantly different and were in accordance with Turkish nasal harmony. Columellar inverted “V” incisions were performed in 15 (53.6%) patients while “V” incisions were used in 13 (46.4%) patients. Fitzpatrick skin Type 4 was seen in 46.42% of the patients, Fitzpatrick Type 3 in 46.42% and Fitzpatrick Type 2 in 7.14% of the patients. No significant difference was seen between columellar scar scores according to skin type and columellar incision type used for external septorhinoplasty.ConclusionsThis study demonstrated that outcomes for nasofrontal angle, nasolabial angle and nasal tip projection ratios analyzed using the Rhinobase program in patients who underwent external septorhinoplasty were similar to reference values for the Turkish population.

Plant and crop production plays a vital role in the overall development of any country. Sugarcane is one of the most produced crops in Australia. Sugarcane plants are vulnerable to various leaf diseases. Early leaf disease detection is crucial for agricultural productivity. Traditional machine learning often relies on handcrafted features, struggles with high-dimensional data, and has scalability issues. Deep learning models overcomes these problems, still there are some challenges, such as the overfitting issue, the need for a large dataset, and less adaptability to new classes. This research addresses the challenge of limited dataset availability of sugarcane leaf diseases by employing few shot learning (FSL) and episodic training. This research proposes a Prototypical Network (PN) based few-shot learning technique to classify sugarcane leaf diseases. The proposed technique uses pretrained deep learning models for feature extraction, such as MobileNet, ResNet, EfficientNet, and PN based FSL to learn from fewer training examples. The experimental results show that the PN with a backbone of ResNet achieved the highest test accuracy of 87 %. The analysis of the experimental results indicates that PN based few shot learning with deep learning models as backbone outperforms standalone pre-trained deep learning models while using limited number of sugarcane images.

SAPENet: Self-Attention based Prototype Enhancement Network for Few-shot Learning

IntroductionKnee injuries, especially Anterior Cruciate Ligament (ACL) tears and meniscus tears, are becoming increasingly common and can severely restrict mobility and quality of life. Early diagnosis is essential for effective treatment and for preventing long-term complications such as knee osteoarthritis. While deep learning approaches have shown promise in identifying knee injuries from MRI scans, they often require large amounts of labeled data, which can be both scarce and privacy-sensitive.MethodsThis paper analyses a hybrid methodology that integrates few-shot learning with federated learning for the diagnosis of knee injuries using MRI scans. The proposed model used a 3DResNet50 architecture as the backbone to enhance both feature extraction and embedding representation. A combined Centralized and Federated Few-Shot Learning Framework is analysed to leverage episodic-intermittent training strategy based on Prototypical Networks. The model is trained incorporating Stochastic Gradient Descent (SGD), Cross-Entropy Loss, and a MultiStep Learning Rate scheduler to enhance few-shot classification. This model also addressed the challenge of limited annotated data ensuring patient data privacy through distributed learning across multiple regions.ResultsThe models performance was evaluated on the MRNet dataset for multi-label classification. In the centralized setting, the model achieved accuracies of 85.3% on axial views, 82.1% on sagittal views, and 71% on coronal views. The propose work attained accuracies as 83% (axial), 83.9% (sagittal), and 65% (coronal), demonstrating the framework’s effectiveness across different learning configurations.DiscussionThe proposed method outperforms in diagnostic accuracy, generalization across MRI planes, and patient privacy via federated learning. However, it faces limitations, including lower coronal view performance and high computational demands due to its complex architecture.

With the successful development of deep learning techniques in recent years, deep neural networks have achieved excellent results in both computer vision and natural language processing by relying on large-scale datasets but still face significant challenges in solving the problem of learning from few-shot. Inspired by the ability of humans to learn to recognize objects as a way to simulate the cognitive process of learning from a small sample size, few-shot learning is a hot topic of research in deep neural networks today. It is also a significant and challenging problem. This paper first introduces the research background and definition of few-shot learning, introduces the relevant models, and summarizes and analyzes the common approaches to the problem of few-shot learning based on deep neural networks at the present stage, which are divided into four types: data augmentation, model fine-tuning, metric learning and meta-learning. Finally, popular datasets for few-shot learning are described, the paper is concluded and future research directions are discussed.

ABSTRACTBreast cancer (BC) detection based on mammogram images is still an open issue, particularly when there is little annotated data. Combining few‐shot learning (FSL) with transfer learning (TL) has been identified as a potential solution to overcome this problem due to its ability to learn from a few examples while producing robust features for classification. The objective of this study is to use and analyze FSL integrated with TL to enhance the classification accuracy and generalization ability in a limited dataset. The proposed approach integrates the FSL models (prototypical networks, matching networks, and relation networks) with the TL procedures. The models are trained using a small set of samples with annotation and can be assessed using various performance metrics. The models were trained and compared to the TL and the state‐of‐the‐art methods regarding accuracy, precision, recall, F1‐score, and area under the ROC curve (AUC). The models proved to be effective when integrated, and the relation networks model was the most accurate, with an accuracy of 95.6% and an AUC of 0.970. The models provided higher accuracy, recall, and F1‐scores, especially in the case of discerning between normal, benign, and malignant cases, as compared to TL traditional techniques and the various recent state‐of‐the‐art techniques. This integrated approach gives high efficiency, accuracy, and scalability to the whole BC detection process, and it has potential for further medical imaging domains. Future research will explore hyperparameter tuning and incorporating electronic health record systems to enhance diagnostic precision and individualized care.

BackgroundDetection of ossification areas of hand bones in X-ray images is an important task, e.g. as a preprocessing step in automated bone age estimation. Deep neural networks have emerged recently as de facto standard detection methods, but their drawback is the need of large annotated datasets. Finetuning pre-trained networks is a viable alternative, but it is not clear a priori if training with small annotated datasets will be successful, as it depends on the problem at hand. In this paper, we show that pre-trained networks can be utilized to produce an effective detector of ossification areas in pediatric X-ray images of hands.Methods and findingsA publicly available Faster R-CNN network, pre-trained on the COCO dataset, was utilized and finetuned with 240 manually annotated radiographs from the RSNA Pediatric Bone Age Challenge, which comprises over 14.000 pediatric radiographs. The validation is done on another 89 radiographs from the dataset and the performance is measured by Intersection-over-Union (IoU). To understand the effect of the data size on the pre-trained network, subsampling was applied to the training data and the training was repeated. Additionally, the network was trained from scratch without any pre-trained weights. Finally, to understand whether the trained model could be useful, we compared the inference of the network to an annotation of an expert radiologist. The finetuned network was able to achieve an average precision (mAP@0.5IoU) of 92.92 ± 1.93. Apart from the wrist region, all ossification areas were able to benefit from more data. In contrast, the network trained from scratch was not able to produce any correct results. When compared to the annotations of the expert radiologist, the network was able to localize the regions quite well, as the F1-Score was on average 91.85 ± 1.06.ConclusionsBy finetuning a pre-trained deep neural network, with 240 annotated radiographs, we were able to successfully detect ossification areas in prediatric hand radiographs.