Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network

Aldhyani, Theyazn H. H.; Verma, Amit; Al-Adhaileh, Mosleh Hmoud; Koundal, Deepika

doi:10.3390/diagnostics12092048

Cited by 29 publications

(8 citation statements)

References 46 publications

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“…The results demonstrate that the retrieved characteristics from the convolutional neural network may help enhance the classification accuracy of a wide variety of skin lesions. Aldhyani et al [4] propose a CNN-based method that effectively employs kernels and activation functions. The proposed model used just 172,363 parameters but nevertheless managed an astonishing 97.85% accuracy on the test dataset.…”

Section: IImentioning

confidence: 99%

Skin Cancer DetectionSkin Cancer Detection

Rao

2024

IJRASET

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The skin is the largest organ and serves as a barrier to protect the internal organs, as well as the bones and muscles underneath it. When the skin deteriorates, the whole body is affected. Skin cancer is surprisingly common since the skin is so easily damaged by UV rays and other kinds of environmental pollutants. It seems that the two most common forms of skin cancer are benign melanoma and malignant melanoma. Malignant moles are significantly more harmful than benign moles and create bleeding blisters on the skin. They get their name because they resemble the more deadly melanocyte cells. Over 700,000 skin lesions are found annually in the United States, reports the American Cancer Society

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Section: IImentioning

confidence: 99%

Skin Cancer DetectionSkin Cancer Detection

Rao

2024

IJRASET

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“…The 1 × 1 convolution filter that VGG provides is useful for both predictive modeling and classification work. The network employs a technique called multiscaling to boost the quality of the data, which increases the number of inputs and solves the problem of overfitting [11]. The final categorization of the items in the photographs is accomplished via the use of layers that are completely linked.…”

Section: Mobilenet Architecturementioning

confidence: 99%

“…Numerous studies have investigated the important characteristics of autism through a variety of lenses, such as facial-feature extractions [8] using eye-tracking strategies [9], face recognition [10][11][12], bio-medical image analysis [13], application creation [14], and speech recognition [15]. Among these methods, face recognition is particularly useful for determining a person's emotional state, and it has the potential to accurately diagnose autism.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning Algorithms to Identify Autism Spectrum Disorder in Children-Based Facial Landmarks

2023

Self Cite

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People with autistic spectrum disorders (ASDs) have difficulty recognizing and engaging with others. The symptoms of ASD may occur in a wide range of situations. There are numerous different types of functions for people with an ASD. Although it may be possible to reduce the symptoms of ASD and enhance the quality of life with appropriate treatment and support, there is no cure. Developing expert systems for identifying ASD based on the facial landmarks of children is the main contribution for improvements in the healthcare system in Saudi Arabia for detecting ASD at an early stage. However, deep learning algorithms have provided outstanding performances in a variety of pattern-recognition studies. The use of techniques based on convolutional neural networks (CNNs) has been proposed by several scholars to use in investigations of ASD. At present, there is no diagnostic test available for ASD, making this diagnosis challenging. Clinicians focus on a patient’s behavior and developmental history. Therefore, using the facial landmarks of children has become very important for detecting ASDs as the face is thought to be a reflection of the brain; it has the potential to be used as a diagnostic biomarker, in addition to being an easy-to-use and practical tool for the early detection of ASDs. This study uses a variety of transfer learning approaches observed in deep CNNs to recognize autistic children based on facial landmark detection. An empirical study is conducted to discover the ideal settings for the optimizer and hyperparameters in the CNN model so that its prediction accuracy can be improved. A transfer learning approach, such as MobileNetV2 and hybrid VGG19, is used with different machine learning programs, such as logistic regression, a linear support vector machine (linear SVC), random forest, decision tree, gradient boosting, MLPClassifier, and K-nearest neighbors. The deep learning models are examined using a standard research dataset from Kaggle, which contains 2940 images of autistic and non-autistic children. The MobileNetV2 model achieved an accuracy of 92% on the test set. The results of the proposed research indicate that MobileNetV2 transfer learning strategies are better than those developed in existing systems. The updated version of our model has the potential to assist physicians in verifying the accuracy of their first screening for ASDs in child patients.

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“…VGG generates a 1 × 1 convolution filter that aids in prediction and categorization. The network improves the data by using a multiscaling technique that increases the number of inputs and eliminates the overfitting problem [ 11 ]. The network’s main difficulty is that training takes a long time, and the network’s weights need more space and bandwidth [ 12 ].…”

Section: Related Workmentioning

confidence: 99%

Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method

et al. 2022

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Breast cancer can develop when breast cells replicate abnormally. It is now a worldwide issue that concerns people’s safety all around the world. Every day, women die from breast cancer, which is especially common in the United States. Mammography, CT, MRI, ultrasound, and biopsies may all be used to detect breast cancer. Histopathology (biopsy) is often carried out to examine the image and discover breast cancer. Breast cancer detection at an early stage saves lives. Deep and machine learning models aid in the detection of breast cancer. The aim of the research work is to encourage medical research and the development of technology by employing deep learning models to recognize cancer cells that are small in size. For histological annotation and diagnosis, the proposed technique makes use of the BreCaHAD dataset. Color divergence is caused by differences in slide scanners, staining procedures, and biopsy materials. To avoid overfitting, we used data augmentation with 19 factors, such as scale, rotation, and gamma. The proposed hybrid dilation deep learning model is of two sorts. It illustrates edges, curves, and colors, and it improves the key traits. It utilizes dilation convolution and max pooling for multi-scale information. The proposed dilated unit processes the image and sends the processed features to the Alexnet, and it can recognize minute objects and thin borders by using the dilated residual expanding kernel model. An AUC of 96.15 shows that the new strategy is better than the old one.

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Multi-Class Skin Lesion Classification Using a Lightweight Dynamic Kernel Deep-Learning-Based Convolutional Neural Network

Cited by 29 publications

References 46 publications

Skin Cancer DetectionSkin Cancer Detection

Skin Cancer DetectionSkin Cancer Detection

Deep Learning Algorithms to Identify Autism Spectrum Disorder in Children-Based Facial Landmarks

Deep Learning Model for the Detection of Real Time Breast Cancer Images Using Improved Dilation-Based Method

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