Skin Diseases Classification Using Deep Leaning Methods (original) (raw)
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Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures
Skin Lesion Classification Based on Deep Convolutional Neural Networks Architectures, 2021
Skin cancer is among the primary cancer types that manifest due to various dermatological disorders, which may be further classified into several types based on morphological features, color, structure, and texture. The mortality rate of patients who have skin cancer is contingent on preliminary and rapid detection and diagnosis of malignant skin cancer cells. Limitations in current dermoscopic images, including shadow, artifact, and noise, affect image quality, which may hamper detection effort. Attempts to overcome these challenges have been made by analyzing the images using deep learning neural networks to perform skin cancer detection. In this paper, the authors review the state-of-the-art in authoritative deep learning concepts pertinent to skin cancer detection and classification.
Deep Features to Classify Skin Lesions
Diagnosing an unknown skin lesion is the first step to determine appropriate treatment. We demonstrate that a linear classifier, trained on features extracted from a convolutional neural network pretrained on natural images, distinguishes among up to ten skin lesions with a higher accuracy than previously published state-of-the-art results on the same dataset. Further, in contrast to competing works, our approach requires no lesion segmentations nor complex preprocessing. We gain consistent additional improvements to accuracy using a per image normalization, a fully convolutional network to extract multi-scale features, and by pooling over an augmented feature space. Compared to state-of-theart, our proposed approach achieves a favourable accuracy of 85.8% over 5-classes (compared to 75.1%) with noticeable improvements in accuracy for underrepresented classes (e.g., 60% compared to 15.6%). Over the entire 10-class dataset of 1300 images captured from a standard (non-dermoscopic) camera, our method achieves an accuracy of 81.8% outperforming the 67% accuracy previously reported.
Skin Lesion Classification Based on Convolutional Neural Networks
2019
Melanoma causes the majority of skin cancer deaths. The population level of melanoma has increased over the past 30 years. It kills around 9.320 people in the US every year. Melanoma can often be found early, when it is most likely to be cured. Medical diagnoses using digital imaging with machine learning methods have become popular because of their ability to recognize patterns in digital images. Image diagnosis accuracy allows disease cured at an early stage. This paper proposes a simulation that can be used for early detection of skin cancer that can help dermatologists to distinguish melanomas from other pigmented lesions on the skin. Some researchers have developed a system using machine learning algorithms used to classify skin lesions from dermoscopy images of human skin. In this study, we proposed Convolutional Neural Network (CNN) to our model. CNN is very efficient for image processing because feature extractors can be optimized, applied to each feature image position. The...
Dermatological Classification Using Deep Learning of Skin Image and Patient Background Knowledge
International Journal of Machine Learning and Computing, 2019
Skin cancer is one of the most common human malignancies. It is a kind of skin diseases caused by abnormal growth of skin cells. Clinically, dermatological disease including skin cancer can be divided into many types. Treatment options for each type are varying depending on the prognosis of a disease. Type of skin disease or dermatological classification is an initial process of clinical screening. Traditional method of initial clinical screening requires a visual diagnosing by specialized expertise. In case the disease is classified as a type of skin cancers, it is a serious case of dermatological disease that should be treated promptly. Therefore, an automatic approach applied for this classification task is very useful. In this work, we propose an automatic method for skin disease classification using deep learning model of convolution neural network, or CNN. In order to increase the classification performance of CNN, we employ both image data and background knowledge of the patient in the modeling process. The experimental results performed on a public dataset show that the CNN model can classify skin diseases with 79.29% accuracy, while our proposed method to incorporate background knowledge of patient in the modeling phase can improve the accuracy up to 80.39%.
Skin Lesion Image Classification using Convolutional Neural Network
Kinetik : game technology, information system, computer network, computing, electronics, and control, 2021
Classification of skin cancer is an important task to detect skin cancer and help with the treatment of skin cancer according to its type. There are many techniques in imaging used to classify skin cancer, one of the superior deep learning (DL) algorithms for classification is the Convolutional Neural Network (CNN). One type of skin cancer is dangerous is melanoma. In this study, CNN is proposed to help classify this type of skin cancer. The dataset consists of 15103 images of skin cancer pigments with 7 different types of skin cancer. These three tests proved malignant skin lesions can be classified with higher accuracy than non-melanocytic skin lesions which is 90% and performance evaluation shows melanocytic and non-melanocytic skin lesions detected with the highest accuracy. The tests conducted in this study grouped several types of skin diseases namely the first tests conducted using a group of melanocytic and non-melanocytic skin disease, second testing using groups of melanoma and melanocytic nevus diseases, and the final testing using malignant and benign. The proposed CNN model achieved significant performance with a best accuracy of 94% on the classification of melanoma and melanocytic nevus.
Six skin diseases classification using deep convolutional neural network
International Journal of Electrical and Computer Engineering (IJECE)
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 ...
JMIR Dermatology, 2020
Background Skin cancer is the most common cancer and is often ignored by people at an early stage. There are 5.4 million new cases of skin cancer worldwide every year. Deaths due to skin cancer could be prevented by early detection of the mole. Objective We propose a skin lesion classification system that has the ability to detect such moles at an early stage and is able to easily differentiate between a cancerous and noncancerous mole. Using this system, we would be able to save time and resources for both patients and practitioners. Methods We created a deep convolutional neural network using an Inceptionv3 and DenseNet-201 pretrained model. Results We found that using the concepts of fine-tuning and the ensemble learning model yielded superior results. Furthermore, fine-tuning the whole model helped models converge faster compared to fine-tuning only the top layers, giving better accuracy overall. Conclusions Based on our research, we conclude that deep learning algorithms are hi...
Biomolecules, 2020
Recent studies have demonstrated the usefulness of convolutional neural networks (CNNs) to classify images of melanoma, with accuracies comparable to those achieved by dermatologists. However, the performance of a CNN trained with only clinical images of a pigmented skin lesion in a clinical image classification task, in competition with dermatologists, has not been reported to date. In this study, we extracted 5846 clinical images of pigmented skin lesions from 3551 patients. Pigmented skin lesions included malignant tumors (malignant melanoma and basal cell carcinoma) and benign tumors (nevus, seborrhoeic keratosis, senile lentigo, and hematoma/hemangioma). We created the test dataset by randomly selecting 666 patients out of them and picking one image per patient, and created the training dataset by giving bounding-box annotations to the rest of the images (4732 images, 2885 patients). Subsequently, we trained a faster, region-based CNN (FRCNN) with the training dataset and check...
Skin Lesions Detection and Classification Using Deep Learning
International Journal of Advanced Trends in Computer Science and Engineering , 2021
The skin lesion is considered the most widespread malignant disease in individuals, and melanoma is the deadliest form of the disease. Early detection affects the prognosis of the disease and improves the chances of survival. Dermatologists use scientific calculation tools such as ABCD to diagnose melanoma through visual inspection of the mole. However, computer vision tools have been presented to support the quantitative scrutiny of skin lesions. Significant improvements to deep learning algorithms in image recognition tasks should be very successful in medical image examination, particularly in the classification of skin lesions used to diagnose melanoma. In this research, a deep learning simulation with 38 layers to detect and classify skin lesions was proposed. Two datasets were used for training and testing i.e., the HAM10000 dataset & the ISIC2019 dataset. Experimental results show that the model outperforms on both the datasets hence making it nondependent of the dataset. 94.45% of validation top 3 accuracies are achieved on the HAM10000 dataset & 93.06% of validation top 3 accuracies are achieved on the ISIC2019 dataset.