Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

He, Xin; Liu, Xiaoming; Wu, Zhen; Wu, Yu; Chu, Xiaowen; Wang, Shihao; Shi, Shaohuai; Tang, Zhenheng; Wang, Yuxin; Zhao, Zhihao; Dai, Jing; Ni, Ronghao; Zhang, Xiaofeng

doi:10.1109/bigdata47090.2019.9006528

Cited by 12 publications

(11 citation statements)

References 23 publications

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“…Several research studies have been proposed in the literature aiming to improve the accuracy of skin diagnosis [63][64][65][66][67][68][69][70][71][72]. Convolutional neural networks (CNN) are adopted in most proposals [63][64][65][66][67][68][69][70][71], except in [72] where the authors proposed fuzzy classification for skin lesion segmentation. Some proposals have considered other information or data in the diagnosis process such as demographic and medical history [66] and sonification (audio) [73].…”

Section: Skin Lesion Diagnosismentioning

confidence: 99%

“…Some proposals have considered other information or data in the diagnosis process such as demographic and medical history [66] and sonification (audio) [73]. Pretrained CNN models have been retrained and evaluated in [63,[65][66][67][68][69]71] and multiple CNN models have been ensembled in [64,66,69,70,73]. A review of DL segmentation, classification, and pre-processing techniques for skin lesion detection is provided in [74].…”

Section: Skin Lesion Diagnosismentioning

confidence: 99%

“…Interactive Atlas of Dermoscopy (IAD) [78] and PH2 [79] have also provided a dataset of dermoscopy images. He et al [71] have collected two datasets, Skin-10 and Skin-100, as part of their research, but they have not been made publicly available. In this research, the HAM10000 (Human Against Machine with 10,000 training images) [80] dataset has been used to train the designed models.…”

Section: Datasetmentioning

confidence: 99%

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Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Janbi

Mehmood

Katib

et al. 2022

Sensors

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Several factors are motivating the development of preventive, personalized, connected, virtual, and ubiquitous healthcare services. These factors include declining public health, increase in chronic diseases, an ageing population, rising healthcare costs, the need to bring intelligence near the user for privacy, security, performance, and costs reasons, as well as COVID-19. Motivated by these drivers, this paper proposes, implements, and evaluates a reference architecture called Imtidad that provides Distributed Artificial Intelligence (AI) as a Service (DAIaaS) over cloud, fog, and edge using a service catalog case study containing 22 AI skin disease diagnosis services. These services belong to four service classes that are distinguished based on software platforms (containerized gRPC, gRPC, Android, and Android Nearby) and are executed on a range of hardware platforms (Google Cloud, HP Pavilion Laptop, NVIDIA Jetson nano, Raspberry Pi Model B, Samsung Galaxy S9, and Samsung Galaxy Note 4) and four network types (Fiber, Cellular, Wi-Fi, and Bluetooth). The AI models for the diagnosis include two standard Deep Neural Networks and two Tiny AI deep models to enable their execution at the edge, trained and tested using 10,015 real-life dermatoscopic images. The services are evaluated using several benchmarks including model service value, response time, energy consumption, and network transfer time. A DL service on a local smartphone provides the best service in terms of both energy and speed, followed by a Raspberry Pi edge device and a laptop in fog. The services are designed to enable different use cases, such as patient diagnosis at home or sending diagnosis requests to travelling medical professionals through a fog device or cloud. This is the pioneering work that provides a reference architecture and such a detailed implementation and treatment of DAIaaS services, and is also expected to have an extensive impact on developing smart distributed service infrastructures for healthcare and other sectors.

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Section: Skin Lesion Diagnosismentioning

confidence: 99%

Section: Skin Lesion Diagnosismentioning

confidence: 99%

Section: Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Janbi

Mehmood

Katib

et al. 2022

Sensors

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“…Faster R‐CNN, in particular, works reasonably well with a small dataset 10 . In medicine, Faster R‐CNN has been used for automated detection of the abnormality in X‐ray and clinical images such as lung nodules in computerized tomography scans (CT‐scans), esophageal adenocarcinoma in high‐definition white light endoscopy (HD‐WLE) images, 11 and skin diseases from clinical photography 12 . In dentistry, there are a few studies on automated detection using Faster R‐CNN mainly for the detection of abnormalities on the radiograph.…”

Section: Introductionmentioning

confidence: 99%

Automatic classification and detection of oral cancer in photographic images using deep learning algorithms

Warin

Limprasert

Suebnukarn

et al. 2021

J Oral Pathology Medicine

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Background Oral cancer is a deadly disease among the most common malignant tumors worldwide, and it has become an increasingly important public health problem in developing and low‐to‐middle income countries. This study aims to use the convolutional neural network (CNN) deep learning algorithms to develop an automated classification and detection model for oral cancer screening. Methods The study included 700 clinical oral photographs, collected retrospectively from the oral and maxillofacial center, which were divided into 350 images of oral squamous cell carcinoma and 350 images of normal oral mucosa. The classification and detection models were created by using DenseNet121 and faster R‐CNN, respectively. Four hundred and ninety images were randomly selected as training data. In addition, 70 and 140 images were assigned as validating and testing data, respectively. Results The classification accuracy of DenseNet121 model achieved a precision of 99%, a recall of 100%, an F1 score of 99%, a sensitivity of 98.75%, a specificity of 100%, and an area under the receiver operating characteristic curve of 99%. The detection accuracy of a faster R‐CNN model achieved a precision of 76.67%, a recall of 82.14%, an F1 score of 79.31%, and an area under the precision‐recall curve of 0.79. Conclusion The DenseNet121 and faster R‐CNN algorithm were proved to offer the acceptable potential for classification and detection of cancerous lesions in oral photographic images.

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“…CNNs are currently being developed as tools to assist clinicians in solving various problems and to increase the accuracy of disease detection in radiographic images and clinical images [3]. The CNN-based algorithms, such as faster R-CNN, ResNet, and DenseNet, have been used to detect and classify lesions in chest x-rays [4] and lesions from clinical images of the skin, cervix, esophagus and larynx, with expert level results [5][6][7][8]. The advent of AI technology does not mean the ultimate replacement of clinicians.…”

Section: Introductionmentioning

confidence: 99%

AI-based Analysis of Oral Lesions Using Novel Deep Convolutional Neural Networks for Early Detection of Oral Cancer

Warin¹,

Limprasert²,

Suebnukarn³

et al. 2021

Preprint

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Background: Artificial intelligence (AI) applications in oncology have been developed rapidly with reported successes in recent years. This work aims to evaluate the performance of deep convolutional neural network (CNN) algorithms for the classification and detection of oral potentially malignant disorders (OPMDs) and oral squamous cell carcinoma (OSCC) in oral photographic images.Methods: A dataset comprising 980 oral photographic images was divided into 365 images of OSCC, 315 images of OPMDs and 300 images of non-pathological images. Multiclass image classification models were created by using DenseNet-169, ResNet-101, SqueezeNet and Swin-S. And multiclass object detection models were fabricated by using faster R-CNN, YOLOv5, RetinaNet and CenterNet2.Results: The AUC of multiclass image classification of CNN models was 0.71-1.00 and 0.80- 0.98 on OSCC and OPMDs, respectively. The AUC of multiclass CNN-base object detection models was 0.81-0.91 and 0.34-0.64 on OSCC and OPMDs, respectively. In comparison, CNN- based classification models exhibited a sensitivity and specificity of 0.72-0.99, 0.83-0.99 on OSCC and 0.74-0.95, 0.88-0.97 on OPMDs, respectively. These values were inline with the performance of oral and maxillofacial surgeons and superior to those of general practictioners (GPs).Conclusions: CNN-based models have potential for the identification of OSCC and OPMDs in oral photographic images and are expected to be a diagnostic tool to assist GPs for the early detection of oral cancer.

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Computer-Aided Clinical Skin Disease Diagnosis Using CNN and Object Detection Models

Cited by 12 publications

References 23 publications

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Imtidad: A Reference Architecture and a Case Study on Developing Distributed AI Services for Skin Disease Diagnosis over Cloud, Fog and Edge

Automatic classification and detection of oral cancer in photographic images using deep learning algorithms

AI-based Analysis of Oral Lesions Using Novel Deep Convolutional Neural Networks for Early Detection of Oral Cancer

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