A Smartphone-Based Skin Disease Classification Using MobileNet CNN

Velasco, Jessica S.; Pascion, Cherry G.; Alberio, Jean Wilmar; Apuang, Jonathan; Cruz, John Stephen; Gomez, Mark Angelo; Molina, Benjamin Jr.; Tuala, Lyndon; Thio-ac, August C.; Jorda, Romeo Jr. L.

doi:10.30534/ijatcse/2019/116852019

Cited by 73 publications

(46 citation statements)

References 18 publications

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“…The implemented model was based on CNN for the recognition of a group of oral lesions, using the Mobilenet V2 network pre-trained with ImageNet, which is characterized by being a large database with several categories including plants, flowers, animals, objects, among others, with excellent results at the time of classification [11,12]. This is the reason why this model was considered to perform the learning transfer.…”

Section: Discussionmentioning

confidence: 99%

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Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

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Image recognition and processing is a suitable tool in systems using machine learning methods. The addition of smartphones as complementary tools in the health area for diagnosis is a fact nowadays due to the advantages they present. Following the trend of providing tools for diagnosis, this research aimed to develop a prototype mobile application for the identification of oral lesions, including potentially malignant lesions, based on convolutional neural networks, as early detection of indications of possible types of cancer in the oral cavity. A mobile application was developed for the Android operating system that implemented the TensorFlow library and the Mobilenet V2 convolutional neural network model. The training of the model was performed by transfer learning with a database of 500 images distributed in five classes for recognition (Leukoplakia, Herpes Simplex Virus Type 1, Aphthous stomatitis, Nicotinic stomatitis, and No lesion). The 80% of the images were used for training and 20% for validation. It was obtained that the application presented at least 80% precision in the recognition of four class. The f1-score and area under curve metrics were used to evaluate performance. The developed mobile application presented an acceptable performance with metrics higher than 75% for the recognition of three lesions, on the other hand, it yielded an unfavorable performance lower than 70% for identifying nicotinic stomatitis cases with the chosen dataset.

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Section: Discussionmentioning

confidence: 99%

“…This is the reason why this model was considered to perform the learning transfer. Lesion recognition systems using learning transfer have been described in the literature with the AlexNet [11], VGGNet [13,14], and ResNet [12,[15][16][17][18] network models, whose performance is similar to the model used for this work.…”

Section: Discussionmentioning

confidence: 99%

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Quintero-Rojas¹,

González²

2021

Rev. Fac. Ing.

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“…The TensorFlow Object Detection API library comprises of object detection structures, such as Single Shot Detector (SSD), Faster Region-based Convolutional Neural Network (Faster R-CNN), etc. Feature extractors such as Inception, MobileNet [16] and Resnet play critical roles in the speed and accuracy trade-off of the framework. Even with the recent studies of various researchers, constructing convolutional networks from scratch requires a great volume of image datasets and a long period of training and testing time.…”

Section: B Tensorflow Object Detection Apimentioning

confidence: 99%

“…In selecting a pre-trained model, the performance, speed, and mean Average Precision (mAP) that define the accuracy of the detector were considered, as in [16] [18]. According to the analysis of Huang et al [15], the Faster R-CNN model with Inception V2 and SSD model with Inception V2 yields a mAP of 28 and 24, respectively, which requires a speed of at least 58 ms and 42 ms per image, respectively.…”

Section: Configuring the Pipelinementioning

confidence: 99%

Automated Estrus Detection for Dairy Cattle through Neural Networks and Bounding Box Corner Analysis

Arago¹,

Alvarez²,

Mabale³

et al. 2020

IJACSA

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Thorough and precise estrus detection plays a crucial role in the fertility of dairy cows. Farmers commonly used direct visual monitoring in recognizing estrus signs which demands time and effort and causes misinterpretations. The primary sign of estrus is the standing heat, where the dairy cows stand to be mounted by other cows for a few seconds. Through the years, researchers developed various detection methods, yet most of these methods involve contact and invasive approaches that affect the estrus behaviors of cows. So, the proponents developed a non-invasive and non-contact estrus detection system using image processing to detect standing heat behaviors. Through the TensorFlow Object Detection API, the proponents trained two custom neural network models capable of visualizing bounding boxes of the predicted cow objects on image frames. The proponents also developed an object overlapping algorithm that utilizes the bounding box corners to detect estrus activities. Based on the conducted tests, an estrus event occurs when the centroids of the detected objects measure a distance of less than 360px and have two interior angles with another fixed point of less than 25° and greater than 65° for Y and X axes, respectively. If the conditions are met, the program will save the image frame and will declare an estrus activity. Otherwise, it will restart its estrus detection and counting. The system observed 17 cows, a carabao, and a bull through the cameras installed atop of a cowshed, and detects the estrus events with an efficiency of 50%.

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“…This results in ten validations, with a total of 320 images, 40 images from each class. The performance of the networks is measured by calculating accuracy, precision, sensitivity, specificity, F1 score, and Mathews correlation coefficient (MCC) for all the seven macular deformities as well as normal cases from the confusion matrix [28]. From the results, it is observed that ResNet performs better than the other two CNNs as shown in Table 2.…”

Section: Development Of An Intelligent System For Dme Categorisationmentioning

confidence: 99%

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

Padmasini

Umamaheswari²

2020

IET image process

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Diabetic Maculopathy, the major cause of vision loss, occurs due to uncontrolled diabetes. This affects the retinal layers of the eye causing bleeding of vessels, which results in Diabetic Macular Edema (DME), macular detachment etc., Basically three structural changes are involved in DME viz., Cystoid Macular Edema (CME), Serous Macular Detachment (SMD) and Intra Retinal Fluid (IRF). These changes may also coexist with each other such as CME with SMD or CME with IRF etc. In this work, the retinal images acquired through Spectral Domain Optical Coherence Tomography (SDOCT) imaging modality, which would provide high level of precision and resolution of the retinal layers are utilized. An automated algorithm to detect and differentiate seven types of DME based on deep learning is implemented using transfer learning on three Convolutional Neural Networks (CNNs), ResNet 50, VGGNet and AlexNet. In detection of DME, ResNet 50 performs excellently well, when compared with Alexnet and VGGNet, because of its depth and skip connection features. The average values of the statistical parameters such as accuracy (0.993), F1 score (0.975), Mathews Correlation Coefficient(MCC)(0.972) of ResNet are high when compared to that of AlexNet and VGGNet

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A Smartphone-Based Skin Disease Classification Using MobileNet CNN

Cited by 73 publications

References 18 publications

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Use of Convolutional Neural Networks in Smartphones for the Identification of Oral Diseases Using a Small Dataset

Automated Estrus Detection for Dairy Cattle through Neural Networks and Bounding Box Corner Analysis

Automated detection of multiple structural changes of diabetic macular oedema in SDOCT retinal images through transfer learning in CNNs

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