57 research outputs found
Six skin diseases classification using deep convolutional neural network
Smart imaging-based medical classification systems help the human diagnose the diseases and make better decisions about patient health. Recently, computer-aided classification of skin diseases has been a popular research area due to its importance in the early detection of skin diseases. This paper presents at its core, a system that exploits convolutional neural networks to classify color images of skin lesions. It relies on a pre-trained deep convolutional neural network to classify between six skin diseases: acne, athlete’s foot, chickenpox, eczema, skin cancer, and vitiligo. Additionally, we constructed a dataset of 3000 colored images from several online datasets and the Internet. Experimental results are encouraging, where the proposed model achieved an accuracy of 81.75%, which is higher than the state of the art researches in this field. This accuracy was calculated using the holdout method, where 90% of the images were used for training, and 10% of the images were used for out-of-sample accuracy testing
DermX: an end-to-end framework for explainable automated dermatological diagnosis
Dermatological diagnosis automation is essential in addressing the high
prevalence of skin diseases and critical shortage of dermatologists. Despite
approaching expert-level diagnosis performance, convolutional neural network
(ConvNet) adoption in clinical practice is impeded by their limited
explainability, and by subjective, expensive explainability validations. We
introduce DermX and DermX+, an end-to-end framework for explainable automated
dermatological diagnosis. DermX is a clinically-inspired explainable
dermatological diagnosis ConvNet, trained using DermXDB, a 554 image dataset
annotated by eight dermatologists with diagnoses, supporting explanations, and
explanation attention maps. DermX+ extends DermX with guided attention training
for explanation attention maps. Both methods achieve near-expert diagnosis
performance, with DermX, DermX+, and dermatologist F1 scores of 0.79, 0.79, and
0.87, respectively. We assess the explanation performance in terms of
identification and localization by comparing model-selected with
dermatologist-selected explanations, and gradient-weighted class-activation
maps with dermatologist explanation maps, respectively. DermX obtained an
identification F1 score of 0.77, while DermX+ obtained 0.79. The localization
F1 score is 0.39 for DermX and 0.35 for DermX+. These results show that
explainability does not necessarily come at the expense of predictive power, as
our high-performance models provide expert-inspired explanations for their
diagnoses without lowering their diagnosis performance
A Mobile-Based Skin Disease Identification System Using Convolutional Neural Networks
Skin diseases pose significant challenges in the
field of dermatology. In recent years, Convolutional
Neural Networks (CNNs) have emerged as a powerful
tool for image recognition and analysis tasks. This
research paper presents a comprehensive study on the
application of CNNs for skin disease diagnosis.
We propose a CNN-based framework for skin
disease diagnosis, which utilizes a large dataset of
dermatological images to accurately identify various skin
diseases. The proposed model leverages the deep
learning capabilities of CNNs to learn discriminative
features from input images, enabling accurate and
efficient diagnosis. We demonstrate improved accuracy
and efficiency in skin disease diagnosis by employing
pre-trained models. Our proposed model enables
accurate classification of skin diseases into high,
medium, and low severity categories by leveraging a
large dataset of annotated images, assisting healthcare
professionals in prioritizing treatment strategies.
In conclusion, this research paper presents a
comprehensive study on the application of CNNs for skin
disease diagnosis, skin lesion classification, melanoma
skin cancer classification, and skin disease severity
classification. The proposed models showcase significant
advancements in the field of dermatology, providing
accurate and efficient tools for dermatologists and
healthcare professionals.
The findings of this research contribute to
improving the diagnosis, classification, and severity
assessment of skin diseases, ultimately enhancing patient
care and treatment outcomes
Survey on Therapy Prediction using Deep Learning for Pores and Skin Diseases
Introduction: Prediction and detection of skin ailments have generally been a hard and important task for health care specialists. In the cutting-edge situation majority of the pores and skin care practitioners are the uses of traditional techniques to diagnose the ailment which may also take a large amount of time. Skin Diseases are excessive troubles in recent times as it is a consider form of environmental factors, socioeconomic elements, loss of entire weight loss program, and so on. Identifying the particular skin disease by computer vision is introduced as a novel task. Based on skin or pore disease, certain therapy can be suggested. In proposed study there are different applications based on deep learning are studied with computer vision task for better performance of proposed application. Famous deep learning algorithms may include CNN (convolutional neural network) , RNN (Recurrent Neural network), etc.
Objective: To diagnose skin disease with dermoscopic images automatically. Developing automated strategies to improve the accuracy of analysis for multiple psoriasis and skin diseases
Methods: In existing techniques many machine learning models are used which is having high complexity and require more time for analysis. So, in this study different deep learning models are studied for understanding performance difference between different models. This paper is a comparative check about skin illnesses related to ordinary skin issues in addition to cosmetology. Image selection, segmentation of skin disease detection and classification are the important steps can be used for oily, dry, and ordinary pores.
Result: The field of dermatology has seen promising results from studies on various Convolutional Neural Network (CNN) algorithms for classifying skin diseases based on clinical images. These studies have concentrated on utilizing the strength of deep learning and computer vision techniques to classify and diagnose different skin conditions using facial images precisely.
Conclusion: A survey of numerous papers is achieved on basis of technologies used, outcomes with accuracy, moral behavior, and number of illnesses diagnosed, datasets. Different existing research methodologies are compared with present deep learning architectures for understanding superior performance of deep learning models. Using deep learning, we can predict pore and skin diseases. In proposed study, introduction to different algorithms of deep learning which are combined with computer vision tasks to find the skin disease and pore disease are studied. Therapy can be predicted based on type of skin or pore disease
A Review on Explainable Artificial Intelligence for Healthcare: Why, How, and When?
Artificial intelligence (AI) models are increasingly finding applications in
the field of medicine. Concerns have been raised about the explainability of
the decisions that are made by these AI models. In this article, we give a
systematic analysis of explainable artificial intelligence (XAI), with a
primary focus on models that are currently being used in the field of
healthcare. The literature search is conducted following the preferred
reporting items for systematic reviews and meta-analyses (PRISMA) standards for
relevant work published from 1 January 2012 to 02 February 2022. The review
analyzes the prevailing trends in XAI and lays out the major directions in
which research is headed. We investigate the why, how, and when of the uses of
these XAI models and their implications. We present a comprehensive examination
of XAI methodologies as well as an explanation of how a trustworthy AI can be
derived from describing AI models for healthcare fields. The discussion of this
work will contribute to the formalization of the XAI field.Comment: 15 pages, 3 figures, accepted for publication in the IEEE
Transactions on Artificial Intelligenc
Generative Adversarial Networks for anonymous Acneic face dataset generation
It is well known that the performance of any classification model is
effective if the dataset used for the training process and the test process
satisfy some specific requirements. In other words, the more the dataset size
is large, balanced, and representative, the more one can trust the proposed
model's effectiveness and, consequently, the obtained results. Unfortunately,
large-size anonymous datasets are generally not publicly available in
biomedical applications, especially those dealing with pathological human face
images. This concern makes using deep-learning-based approaches challenging to
deploy and difficult to reproduce or verify some published results. In this
paper, we suggest an efficient method to generate a realistic anonymous
synthetic dataset of human faces with the attributes of acne disorders
corresponding to three levels of severity (i.e. Mild, Moderate and Severe).
Therefore, a specific hierarchy StyleGAN-based algorithm trained at distinct
levels is considered. To evaluate the performance of the proposed scheme, we
consider a CNN-based classification system, trained using the generated
synthetic acneic face images and tested using authentic face images.
Consequently, we show that an accuracy of 97,6\% is achieved using
InceptionResNetv2. As a result, this work allows the scientific community to
employ the generated synthetic dataset for any data processing application
without restrictions on legal or ethical concerns. Moreover, this approach can
also be extended to other applications requiring the generation of synthetic
medical images. We can make the code and the generated dataset accessible for
the scientific community
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