Classification of Skin Lesions into Seven Classes Using Transfer Learning with AlexNet.

Skin lesions are one of the most prevalent form of diseases existing among us. Early detection and classification of potentially malignant skin lesions can give us a lead in the fight against skin cancer. There are many lesion classification divisions on medical grounds; however, an automated system that detects and classifies a majority of these classes is not prevalent. In view of this scenario, our proposed study aims to classify the input skin lesion images into nine classes, namely, squamous cell carcinoma (SCC), Basal cell carcinoma (BCC), melanocytic nevi (NV), actinic keratoses and intraepithelial carcinoma (AKIEC), melanoma (MEL), seborrheic keratosis (SEK), dermatofibroma (DF), benign keratosis like lesions (BKL), and vascular lesions (VASC). The proposed methodology uses contrast stretching as an image enhancement technique to facilitate efficient Region of Interest (ROI) segmentation. The novelty of the proposed study lies in the first-hand implementation of Vision Transformer (ViT) for feature extraction in the domain of skin lesion detection. Finally, a light-weight multi-layer perceptron (MLP) composed of fully connected layers is used for multinomial classification. Combining the aforementioned techniques, the proposed method achieves training accuracy of 98% and testing accuracy of about 93.22%. The impressive performance across nine distinct categories represents a significant milestone. This success demonstrates the model’s scalability, suggesting it can be effectively extended to a broader array of diagnostic classes in future research.

Medical image classification is an essential task in the field of combining medical applications with Artificial Intelligence. This study is carried out to introduce an accurate, precise method for skin cancer recognition. This research investigates the performance of classifying skin cancer dataset HAM10000 using ResNet50, MobileNet, and the traditional Support Vector Machines (SVM) model. The dataset combines seven cancer types: Actinic Keratosis, Basal Cell Carcinoma, Benign Keratosis, Dermatofibroma, Melanoma, Melanocytic Nevus, and Vascular Lesion. The SVM classifier is designed to employ a Histogram of Oriented Gradient (HOG) features with Principle Component Analysis (PCA). Moreover, the Synthetic Minority Oversampling Technique is used to balance the dataset. Additionally, six conventional machine learning (ML) methods are used to compare the results with the calculation of precision, recall, F1 Score, and accuracy. The results confirm that the SVM method outperforms the other algorithms with an accuracy of 99.15%. The novelty contribution of this research activity is mainly based on the development of a high accuracy, low computational complex machine method for skin cancer types recognition in the domain of medical image classification. Received: 24 October 2022 | Revised: 5 December 2022 | Accepted: 12 December 2022 Conflicts of Interest The authors declare that they have no conflicts of interest to this work.

Melanoma, one of the most deadly skin lesions is a major cause of death if not detected at earlier stages. Due to the high similarity between melanoma and other similar lesions, the investigation and diagnosis of these lesions are complex and time consuming job. To address such problems, in this paper, we present a machine learning integrated automated lesion classification in Healthcare Cyber-Physical System (HCPS) or smart healthcare framework. We propose an automated classification of various types of skin lesions by using deep learning tools, which save time, human effort, money, and human life by diagnosing melanoma at the earlier stage. Our model can classify 7 different types of skin lesions that are Melanocytic nevi, Melanoma, Benign keratosis, Basal cell carcinoma, Actinic keratoses, Vascular lesions, Dermatofibroma. The purpose of this paper is to classify the skin lesion with our pre-trained model. We have trained our model on multiple platforms like DenseNet169, DenseNet121, ResNet50, ResNet152 and found DenseNet 169 is most accurate among all. Therefore, the proposed technique may be useful for diagnosis of melanoma in rural places, where healthcare facilities, availability of oncologists and doctors are limited.

e22018 Background: The prevalence of skin cancer is increasing worldwide. According to World Health Organization (WHO),there is one in every three cancers diagnosed in US is a skin cancer. Traditional ways for skin cancer diagnosis have shown many limitations: inadequate accuracy, consume much time, and effort. In order to assist dermatologists for earlier and accurate diagnosis, we propose to develop a computer aided diagnosis systems for automatic classification of skin lesions. Deep learning architectures are used in this area based on a new convolutional neural network that can classify skin lesions with improved accuracy. Methods: A public dataset of skin lesions HAM10000 ("Human Against Machine with 10000 training images") is used for training and testing. For the validation of our work, a private dataset is collected from a dermatology office in Besançon (France). This dataset contains 45 different dermatoscopic images of skin lesions (Basal cell carcinoma, squamous cell carcinoma and Actinic keratosis) with their histology results. In this research, a three-phase approach was proposed and implemented: Phase one is preprocessing the data; by amputate missing values using the mean filling method. The dermoscopy images in the dataset were downscaled to 224X224 pixels. Then, data augmentation was applied to solve the imbalanced data problem. Finally, the ten-fold cross-validation method was applied to compare the performance of three CNN architectures used in literature: DenseNet 201, ResNet 152, and VGGNet with our proposed architecture. Results: Results obtained with our model show the highest classification accuracy 0.95, a sensitivity of 0, 96, a specificity of 0.94, and outperforms other algorithms in classifying these skin lesions. Conclusions: Our research improves the performance of computer aided diagnosis systems for skin lesions by giving an accurate classification. The use of this system helps dermatologists to make accurate classification with lower time, cost, and effort. Our future work will focus on generalizing the domain by developing a model that can classify various lesions using various types of data (dermoscopic images, histological images, clinical data, sensors data...etc) using the advanced techniques in literature of transfer learning and adaptors models.

To evaluate the variables possibly related to actinic keratosis and malignant skin lesions on the eyelid. A prospective study of patients with suspected eyelid malignancy was conducted. The participants underwent a 2-mm punch biopsy at two opposite sites of the lesion for diagnosis, and the results were compared with those of the histopathological study of the surgical excised specimen. The patients with an actinic keratosis component were divided into two groups (actinic keratosis-associated malignancy and actinic keratosis alone), which were compared for the following variables: age, disease duration, largest diameter, tumor area, Fitzpatrick classification, sex, tumor site and margin involvement. A cluster analysis was also performed. We analyzed 174 lesions, of which 50 had an actinic keratosis component. Actinic keratosis was associated with squamous cell carcinoma in 22% of the cases and to basal cell carcinoma in 38%, which shows that both neoplasms may have contiguous actinic keratosis. Statistical analysis revealed no significant difference among the variables. In a cluster analysis, four groups were identified with malignant lesions in the medial canthus with the largest mean diameter and area. All margin involvements on the lower eyelid were related to malignancy, which means that all cases with margin involvement had an almost 100% risk of malignancy. Larger actinic keratosis lesions in the medial canthus and lesions with margin involvement on the lower eyelid have a greater probability of malignant association.

Identification of pigmented skin lesions is necessary for the detection of severe diseases associated with the skin organ, notably malignancy. Accurate skin cancer diagnosis can be improved with the use of image detection approaches and computer classification skills. Therefore, this research work plans to perform skin lesion prediction and classification using a novel deep learning methodology. Initially, the data related to the skin lesion are gathered from the ISIC dataset. After collecting the images, the pre-processing is performed using hair removal and filtering hair removed images via median filtering. These pre-processed images undergo segmentation process accomplished using the U-Net method. Next, the features are extracted from these segmented images with the help of color features, and texture features by GLCM and RGB histogram features. These extracted features undergo the prediction phase that is accomplished using the MLSTM model, in which the parameter optimization is done by the nature inspired novel hybrid metaheuristic algorithm referred as SC-STBO algorithm with the consideration of accuracy maximization and RMSE minimization as the major fitness for the objective function. If the predicted output is returned as the presence of skin lesion, the same novel MLSTM model classifies the final skin lesion output into seven types, such as Vascular Lesions, Melanocytic Nevi, Melanoma, Dermatofibroma, Benign Keratosis-like Lesions, BCC, and Actinic Keratoses, respectively. Seven groups of skin diseases can be identified early thanks to the suggested effort, which can then be tested and properly handled by medical professionals. With an accuracy of 0.9931, the recommended methodology clearly outperforms traditional techniques. Similarly, the suggested methodology clearly beats the conventional methods, with a recall of 0.9825.

Skin cancer is one of the most common cancers, and its early detection can have a huge impact on its outcomes. Deep learning, especially convolutional neural networks, perform well in processing massive amounts of data, especially image data in classifying skin cancer. In this paper, convolutional neural networks are mainly used to diagnose and classify 7 types of skin lesions, including melanoma, basal cell carcinoma, melanocytic nevus, actinic keratosis, and intraepithelial carcinoma, benign keratinoid lesions, dermatofibroma, and vascular lesions. First, the characteristics of skin lesion images are analyzed, using image processing technology and sampling technology to preprocess skin lesion images. Then the training parameters of imageNet network are adjusted through the idea of transfer learning on InceptionV3, ResNet50, DenseNet201, and other networks to perform training classification. Furthermore, different convolutional neural network models are built for classification. In order to validate the classification performance of various convolutional neural network models, this paper adopts ISIC 2017 HAM10000 dataset for experiments. The experimental results show that proper preprocessing is necessary when applying CNN for image classification. In classifying the 224*224 skin lesion images, the classical deep convolutional network with DenseNet201 achieved a remarkable performance classification accuracy of 99.12% for training and 86.91% for testing.

TERT promoter mutations in actinic keratosis before and after treatment.

We investigated correlations between cell proliferation, p53 overexpression, and degree of malignancy in cutaneous epithelial neoplasms. One hundred and fourteen cases of epithelial neoplasms, including seborrheic keratosis (SEB), basal cell carcinomas (BCCs), solar keratosis (SK), Bowen's disease (BD), and squamous cell carcinomas (SCCs) were examined using argyrophilic nucleolar organizer region (AgNOR) staining. In addition, immunohistochemical analysis using the Ki-67 (MIB-1) and anti-p53 (DO-7) monoclonal antibodies was performed. The ratio of tumorous to normal cells according to AgNOR staining was defined as the AgNOR rate, and the ratio of tumorous to normal cells according to Ki-67 recognition was defined as the Ki-67 rate. SCC lesions showed the highest AgNOR rate among the investigated epithelial neoplasms, followed in order by BD, BCC, SK, and SEB lesions. The Ki-67 rate was highest in BD lesions, followed in order by SK, SCC, BCC, and SEB lesions. Expression of p53 protein was highest in SK lesions. SCC is generally considered to be the most malignant neoplasm, followed in order by BCC, BD, and SK. Thus, our results suggest that the Ki-67 rate and overexpression of p53 protein do not always reflect the degree of malignancy in neoplasms.

According to Skin Cancer Foundation, skin cancer is by far the most common type of cancer in the United States and worldwide. Early diagnosis of skin cancer is critical because proper treatment at early stages can increase the chance of cure and recovery. However, visual inspection of dermoscopic images by dermatologists is error-prone and time-consuming. To ensure accurate diagnosis and faster treatment of skin cancer, deep learning techniques have been utilized to conduct automated skin lesion segmentation and classification. In this paper, after image processing, a Mask R-CNN model is built for lesion segmentation, where transfer learning is utilized by using the pre-trained weights from Microsoft COCO dataset. The weights of the trained Mask R-CNN model are saved and transferred to the next task - skin lesion classification, to train a Mask R-CNN model for classification. Our experiments are conducted on the benchmark datasets from the International Skin Imaging Collaboration 2018 (ISIC 2018) and evaluated by the same metrics used in ISIC 2018. The lesion boundary segmentation and lesion classification have achieved an accuracy of 96% and a balanced multiclass accuracy of 80%, respectively.

Concern about skin cancer is a common reason for people from predominantly fair-skinned populations to present to primary care doctors. To examine the frequency and body-site distribution of malignant, pre-malignant and benign pigmented skin lesions excised in primary care. This prospective study conducted in Queensland, Australia, included 154 primary care doctors. For all excised or biopsied lesions, doctors recorded the patient's age and sex, body site, level of patient pressure to excise, and the clinical diagnosis. Histological confirmation was obtained through pathology laboratories. Of 9650 skin lesions, 57·7% were excised in males and 75·0% excised in patients ≥ 50 years. The most common diagnoses were basal cell carcinoma (BCC) (35·1%) and squamous cell carcinoma (SCC) (19·7%). Compared with the whole body, the highest densities for SCC, BCC and actinic keratoses were observed on chronically sun-exposed areas of the body including the face in males and females, the scalp and ears in males, and the hands in females. The density of BCC was also high on intermittently or rarely exposed body sites. Females, younger patients and patients with melanocytic naevi were significantly more likely to exert moderate/high levels of pressure on the doctor to excise. More than half the excised lesions were skin cancer, which mostly occurred on the more chronically sun-exposed areas of the body. Information on the type and body-site distribution of skin lesions can aid in the diagnosis and planned management of skin cancer and other skin lesions commonly presented in primary care.

Skin lesion classification has recently attracted significant attention. Regularly, physicians take much time to analyze the skin lesions because of the high similarity between these skin lesions. An automated classification system using deep learning can assist physicians in detecting the skin lesion type and enhance the patient’s health. The skin lesion classification has become a hot research area with the evolution of deep learning architecture. In this study, we propose a novel method using a new segmentation approach and wide-ShuffleNet for skin lesion classification. First, we calculate the entropy-based weighting and first-order cumulative moment (EW-FCM) of the skin image. These values are used to separate the lesion from the background. Then, we input the segmentation result into a new deep learning structure wide-ShuffleNet and determine the skin lesion type. We evaluated the proposed method on two large datasets: HAM10000 and ISIC2019. Based on our numerical results, EW-FCM and wide-ShuffleNet achieve more accuracy than state-of-the-art approaches. Additionally, the proposed method is superior lightweight and suitable with a small system like a mobile healthcare system.

Dermatological problems are the most widely spread skin diseases amongst human beings. They can be infectious, chronic, and sometimes may also lead to serious health problems such as skin cancer. Generally, rural area clinics lack trained dermatologists and mostly rely on the analysis of remotely accessible experts through mobile-based networks for sharing the images and other related information. Under such circumstances, poor image quality introduced due to the capturing device results in misleading diagnosis. Here, a genetic-algorithm- (GA-) based approach used as an image enhancement technique has been explored to improve the low quality of the dermatological images received from the rural clinic. The diagnosis is performed on the enhanced images using convolutional neural network (CNN) classifier for the identification of the diseases. The scope of this paper is limited to only motion blurred images, which is the most prevalent problem in capturing of the images, specifically when any of the two (device or the object) may move unpredictably. Seven types of skin diseases, namely, melanoma, melanocytic nevus, basal cell carcinoma, actinic keratosis, benign keratosis, vascular lesion, and squamous cell carcinoma, have been investigated using ResNet-152 giving an overall accuracy of 87.40% for the blurred images. Use of GA-enhanced images increased the accuracy to 95.85%. The results were further analyzed using a confusion matrix and t-test-based statistical investigations. The advantage of the proposed technique is that it reduces the analysis time and errors due to manual diagnosis. Furthermore, speedy and reliable diagnosis at the earliest stage reduces the risk of developing more severe skin problems.

A machine learning approach for skin disease detection and classification using image segmentation

The medical sciences are facing a major problem with the auto-detection of disease due to the fast growth in population density. Intelligent systems assist medical professionals in early disease detection and also help to provide consistent treatment that reduces the mortality rate. Skin cancer is considered to be the deadliest and most severe kind of cancer. Medical professionals utilize dermoscopy images to make a manual diagnosis of skin cancer. This method is labor-intensive and time-consuming and demands a considerable level of expertise. Automated detection methods are necessary for the early detection of skin cancer. The occurrence of hair and air bubbles in dermoscopic images affects the diagnosis of skin cancer. This research aims to classify eight different types of skin cancer, namely actinic keratosis (AKs), dermatofibroma (DFa), melanoma (MELa), basal cell carcinoma (BCCa), squamous cell carcinoma (SCCa), melanocytic nevus (MNi), vascular lesion (VASn), and benign keratosis (BKs). In this study, we propose SNC_Net, which integrates features derived from dermoscopic images through deep learning (DL) models and handcrafted (HC) feature extraction methods with the aim of improving the performance of the classifier. A convolutional neural network (CNN) is employed for classification. Dermoscopy images from the publicly accessible ISIC 2019 dataset for skin cancer detection is utilized to train and validate the model. The performance of the proposed model is compared with four baseline models, namely EfficientNetB0 (B1), MobileNetV2 (B2), DenseNet-121 (B3), and ResNet-101 (B4), and six state-of-the-art (SOTA) classifiers. With an accuracy of 97.81%, a precision of 98.31%, a recall of 97.89%, and an F1 score of 98.10%, the proposed model outperformed the SOTA classifiers as well as the four baseline models. Moreover, an Ablation study is also performed on the proposed method to validate its performance. The proposed method therefore assists dermatologists and other medical professionals in early skin cancer detection.