Evaluation of deep learning models for melanoma image classification (original) (raw)
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Skin Lesion Classification Using Convolutional Neural Network for Melanoma Recognition
2020
Skin cancer, also known as melanoma, is generally diagnosed visually from the dermoscopic images, which is a tedious and time-consuming task for the dermatologist. Such a visual assessment, via the naked eye for skin cancers, is a challenging and arduous due to different artifacts such as low contrast, various noise, presence of hair, fiber, and air bubbles, etc. This article proposes a robust and automatic framework for the Skin Lesion Classification (SLC), where we have integrated image augmentation, Deep Convolutional Neural Network (DCNN), and transfer learning. The proposed framework was trained and tested on publicly available IEEE International Symposium on Biomedical Imaging (ISBI)-2017 dataset. The obtained average area under the receiver operating characteristic curve (AUC), recall, precision, and F1-score are respectively 0.87, 0.73, 0.76, and 0.74 for the SLC. Our experimental studies for lesion classification demonstrate that the proposed approach can successfully disti...
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...
Convolutional neural network-based skin cancer classification with transfer learning models
2023
Skin cancer is a medical condition characterized by abnormal growth of skin cells. This occurs when the DNA within these skin cells becomes damaged. In addition, it is a prevalent form of cancer that can result in fatalities if not identified in its early stages. A skin biopsy is a necessary step in determining the presence of skin cancer. However, this procedure requires time and expertise. In recent times, artificial intelligence and deep learning algorithms have exhibited superior performance compared with humans in visual tasks. This result can be attributed to improved processing capabilities and the availability of vast datasets. Automated classification driven by these advancements has the potential to facilitate the early identification of skin cancer. Traditional diagnostic methods might overlook certain cases, whereas artificial intelligence-powered approaches offer a broader perspective. Transfer learning is a widely used technique in deep learning, involving the use of pre-trained models. These models are extensively implemented in healthcare, especially in diagnosing and studying skin lesions. Similarly, convolutional neural networks (CNNs) have recently established themselves as highly robust autonomous feature extractors that can achieve excellent accuracy in skin cancer detection because of their high potential. The primary goal of this study was to build deep-learning models designed to perform binary classification of skin cancer into benign and malignant categories. The tasks to resolve are as follows: partitioning the database, allocating 80% of the images to the training set, assigning the remaining 20% to the test set, and applying a preprocessing procedure to the images, aiming to optimize their suitability for our analysis. This involved augmenting the dataset and resizing the images to align them with the specific requirements of each model used in our research; finally, building deep learning models to enable them to perform the classification task. The methods used are a CNNs model and two transfer learning models, i.e., Visual Geometry Group 16 (VGG16) and Visual Geometry Group 19 (VGG19). They are applied to dermoscopic images from the International Skin Image Collaboration Archive (ISIC) dataset to classify skin lesions into two classes and to conduct a comparative analysis. Our results indicated that the VGG16 model outperformed the others, achieving an accuracy of 87% and a loss of 38%. Additionally, the VGG16 model demonstrated the best recall, precision, and F1-score. Comparatively, the VGG16 and VGG19 models displayed superior performance in this classification task compared with the CNN model. Conclusions. The significance of this study stems from the fact that deep learning-based clinical decision support systems have proven to be highly beneficial, offering valuable recommendations to dermatologists during their diagnostic procedures.
Automatic Classification of Melanoma Skin Cancer with Deep Convolutional Neural Networks
AI
Melanoma skin cancer is one of the most dangerous types of skin cancer, which, if not diagnosed early, may lead to death. Therefore, an accurate diagnosis is needed to detect melanoma. Traditionally, a dermatologist utilizes a microscope to inspect and then provide a report on a biopsy for diagnosis; however, this diagnosis process is not easy and requires experience. Hence, there is a need to facilitate the diagnosis process while still yielding an accurate diagnosis. For this purpose, artificial intelligence techniques can assist the dermatologist in carrying out diagnosis. In this study, we considered the detection of melanoma through deep learning based on cutaneous image processing. For this purpose, we tested several convolutional neural network (CNN) architectures, including DenseNet201, MobileNetV2, ResNet50V2, ResNet152V2, Xception, VGG16, VGG19, and GoogleNet, and evaluated the associated deep learning models on graphical processing units (GPUs). A dataset consisting of 71...
BMC Bioinformatics
Background Melanoma results in the vast majority of skin cancer deaths during the last decades, even though this disease accounts for only one percent of all skin cancers’ instances. The survival rates of melanoma from early to terminal stages is more than fifty percent. Therefore, having the right information at the right time by early detection with monitoring skin lesions to find potential problems is essential to surviving this type of cancer. Results An approach to classify skin lesions using deep learning for early detection of melanoma in a case-based reasoning (CBR) system is proposed. This approach has been employed for retrieving new input images from the case base of the proposed system DePicT Melanoma Deep-CLASS to support users with more accurate recommendations relevant to their requested problem (e.g., image of affected area). The efficiency of our system has been verified by utilizing the ISIC Archive dataset in analysis of skin lesion classification as a benign and ...
A Novel Technique on Detect Melanoma in Dermoscopy Images By using Deep Learning
International Journal of Innovative Technology and Exploring Engineering
Melanoma is a typical sort of malignant growth that influences countless. As of late, profound learning strategies have been appeared to be very precise in arranging pictures in different fields. This investigation utilizes profound figuring out how to consequently distinguish melanomas in dermoscopy pictures. To begin with, we preprocess the pictures to evacuate undesirable antiques, for example, hair, and afterward consequently fragment the skin sore. We at that point group the pictures utilizing a convolution neural system. To assess its viability, we test this classifier utilizing both preprocessed and natural pictures from the PH2 dataset. The outcomes a remarkable execution as far as affectability, explicitness, and exactness. Specifically, our methodology was 93% exact in distinguishing the nearness or nonappearance of melanoma, with sensitivities and specificities in the 86%– 94% territory
Diagnosing Melanomas in Dermoscopy Images Using Deep Learning
Diagnostics
When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception...
Melanoma is a kind of skin cancer that develops in melanocyte cells. It is one of the most serious kind of skin cancer, yet it is not as frequent as other types of skin cancer.It is very hard to detect, even under expert supervision. A Deep Convolutional Neural Network (D-CNN) Visual Geometry Group (VGG16) model, is proposed to improve the classification performance of skin lesions.The main disadvantage of using the deep learning methods is that more time is needed for training. Thus, with the help of transfer learning technique, the training time is reduced. The datasets utilized in the proposed strategy to train the model were obtained from International Skin Imaging Collaboration (ISIC).Metrics like Accuracy (ACC), Positive Predictive Value (PPV), Negative Predictive Value (NPV), Specificity (SPC), and Sensitivity (SE) were measured for the evaluation of the classification.The performance of the classification process done by the classifier model on a test data is represented usi...
Deep learning model to improve melanoma detection in people of color
Arab Journal of Basic and Applied Sciences, 2023
Melanoma is a type of skin cancer that is particularly dangerous to people with dark skin. This is due to the disease's late diagnosis and detection in people with dark skin. When melanoma is detected, the prognosis is often poor. Advancement in Artificial Intelligence (AI) technology and image classification has brought about tremendous progress and applications in medicine and diagnosis of skin cancer. Albeit, these techniques continue to produce unsatisfactory results when applied to people with dark skin. This work considered the trend in cancer detection using AI techniques. Dark skin melanoma clinical images were acquired and pre-processed to remove illumination and noise to aid the other stages such as segmentation and data augmentation. The acquired images were combined with a curated and augmented version of the Human Against Machines 10000 image (HAM10000) dataset and split into two classes: melanoma and non-melanoma. A pre-trained DenseNet121 was used as a base model for training and in addition, a transfer learning process was performed to exclude the top layer and fine-tune all the layers in the final Dense Block of the model pretrained on ImageNet. The model achieved an accuracy of 99% for melanoma detection in white skin color and 98% for dark skin. The results show that the proposed model is effective in detecting melanoma in skin.
Melanoma Detection Using Deep Learning-Based Classifications
Healthcare
One of the most prevalent cancers worldwide is skin cancer, and it is becoming more common as the population ages. As a general rule, the earlier skin cancer can be diagnosed, the better. As a result of the success of deep learning (DL) algorithms in other industries, there has been a substantial increase in automated diagnosis systems in healthcare. This work proposes DL as a method for extracting a lesion zone with precision. First, the image is enhanced using Enhanced Super-Resolution Generative Adversarial Networks (ESRGAN) to improve the image’s quality. Then, segmentation is used to segment Regions of Interest (ROI) from the full image. We employed data augmentation to rectify the data disparity. The image is then analyzed with a convolutional neural network (CNN) and a modified version of Resnet-50 to classify skin lesions. This analysis utilized an unequal sample of seven kinds of skin cancer from the HAM10000 dataset. With an accuracy of 0.86, a precision of 0.84, a recall ...