Channel Attention Separable Convolution Network for Skin Lesion Segmentation

Skin cancer is a frequently occurring cancer in the human population, and it is very important to be able to diagnose malignant tumors in the body early. Lesion segmentation is crucial for monitoring the morphological changes of skin lesions, extracting features to localize and identify diseases to assist doctors in early diagnosis. Manual de-segmentation of dermoscopic images is error-prone and time-consuming, thus there is a pressing demand for precise and automated segmentation algorithms. Inspired by advanced mechanisms such as U-Net, DenseNet, Separable Convolution, Channel Attention, and Atrous Spatial Pyramid Pooling (ASPP), we propose a novel network called Channel Attention Separable Convolution Network (CASCN) for skin lesions segmentation. The proposed CASCN is evaluated on the PH2 dataset with limited images. Without excessive pre-/post-processing of images, CASCN achieves state-of-the-art performance on the PH2 dataset with Dice similarity coefficient of 0.9461 and accuracy of 0.9645.Comment: Accepted by ICONIP 202

Automatic Skin Cancer Detection in Dermoscopy Images Based on Ensemble Lightweight Deep Learning Network

The complex detection background and lesion features make the automatic detection of dermoscopy image lesions face many challenges. The previous solutions mainly focus on using larger and more complex models to improve the accuracy of detection, there is a lack of research on significant intra-class differences and inter-class similarity of lesion features. At the same time, the larger model size also brings challenges to further algorithm application; In this paper, we proposed a lightweight skin cancer recognition model with feature discrimination based on fine-grained classification principle. The propose model includes two common feature extraction modules of lesion classification network and a feature discrimination network. Firstly, two sets of training samples (positive and negative sample pairs) are input into the feature extraction module (Lightweight CNN) of the recognition model. Then, two sets of feature vectors output from the feature extraction module are used to train the two classification networks and feature discrimination networks of the recognition model at the same time, and the model fusion strategy is applied to further improve the performance of the model, the proposed recognition method can extract more discriminative lesion features and improve the recognition performance of the model in a small amount of model parameters; In addition, based on the feature extraction module of the proposed recognition model, U-Net architecture, and migration training strategy, we build a lightweight semantic segmentation model of lesion area of dermoscopy image, which can achieve high precision lesion area segmentation end-to-end without complicated image preprocessing operation; The performance of our approach was appraised through widespread experiments comparative and feature visualization analysis, the outcome indicates that the proposed method has better performance than the start-of-the-art deep learning-based approach on the ISBI 2016 skin lesion analysis towards melanoma detection challenge dataset

Polar Contrast Attention and Skip Cross-channel Aggregation for Efficient Learning in U-Net

Peer reviewe

Medical Image Classification Using Transfer Learning and Chaos Game Optimization on the Internet of Medical Things

The Internet of Medical Things (IoMT) has dramatically benefited medical professionals that patients and physicians can access from all regions. Although the automatic detection and prediction of diseases such as melanoma and leukemia is still being researched and studied in IoMT, existing approaches are not able to achieve a high degree of efficiency. Thus, with a new approach that provides better results, patients would access the adequate treatments earlier and the death rate would be reduced. Therefore, this paper introduces an IoMT proposal for medical images classification that may be used anywhere, i.e. it is an ubiquitous approach. It was design in two stages: first, we employ a Transfer Learning (TL)-based method for feature extraction, which is carried out using MobileNetV3; second, we use the Chaos Game Optimization (CGO) for feature selection, with the aim of excluding unnecessary features and improving the performance, which is key in IoMT. Our methodology was evaluated using ISIC-2016, PH2, and Blood-Cell datasets. The experimental results indicated that the proposed approach obtained an accuracy of 88.39% on ISIC-2016, 97.52% on PH2, and 88.79% on Blood-cell. Moreover, our approach had successful performances for the metrics employed compared to other existing methods.Comment: 22 pages, 12 figures, journa

Psoriasis Skin Disease Classification based on Clinical Images

Psoriasis is an autoimmune skin disorder that causes skin plaques to develop into red and scaly patches. It affects millions of people globally. Dermatologists currently employ visual and haptic methods to determine a medical issue's severity. Intelligent medical imaging-based diagnosis systems are now a possibility because of the relatively recent development of deep learning technologies for medical image processing. These systems can help a human expert make better decisions about a patient's health. Convolutional neural networks, or CNNs, on the other hand, have achieved imaging performance levels comparable to, if not better than, those of humans. In the paper, a Dermnet dataset is used. Image preprocessing, fuzzy c-mean-based segmentation, MobileNet-based feature extraction, and a support vector machine (SVM) classification are used for skin disease classification. Dermnet's dataset was investigated for images of skin conditions using three classes Psoriasis, Dermatofibroma, and Melanoma are studied. The performance metrics such as accuracy, precision-recall, and f1-score are evaluated and compared for three classes of skin diseases. Despite working with a smaller dataset, MobileNet with Support Vector Machine outperforms ResNet in terms of accuracy (99.12%), precision (98.65%), and recall (99.66%)

Android skin cancer detection and classification based on MobileNet v2 model

The latest developments in the smartphone-based skin cancer diagnosis application allow simple ways for portable melanoma risk assessment and diagnosis for early skin cancer detection. Due to the trade-off problem (time complexity and error rate) on using a smartphone to run a machine learning algorithm for image analysis, most of the skin cancer diagnosis apps execute the image analysis on the server. In this study, we investigate the performance of skin cancer images detection and classification on android devices using the MobileNet v2 deep learning model. We compare the performance of several aspects; object detection and classification method, computer and android based image analysis, image acquisition method, and setting parameter. Skin cancer actinic Keratosis and Melanoma are used to test the performance of the proposed method. Accuracy, sensitivity, specificity, and running time of the testing methods are used for the measurement. Based on the experiment results, the best parameter for the MobileNet v2 model on android using images from the smartphone camera produces 95% accuracy for object detection and 70% accuracy for classification. The performance of the android app for object detection and classification model was feasible for the skin cancer analysis. Android-based image analysis remains within the threshold of computing time that denotes convenience for the user and has the same performance accuracy with the computer for the high-quality images. These findings motivated the development of disease detection processing on android using a smartphone camera, which aims to achieve real-time detection and classification with high accuracy

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background: Skin diseases are reported to contribute 1.79\% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency. Objective: Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions. Methods: We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance. Results: The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet. Conclusions: To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time

Deep learning techniques applied to skin lesion classification: a review

Skin cancer is one of the most common cancers in the world. The most dangerous type of skin cancer is melanoma, which can be lethal if not treated early. However, diagnosing skin lesions can be a difficult task. Therefore, deep learning techniques applied to the diagnosis of skin lesions have been explored by researchers, given their effectiveness in extracting features and classifying input data. In this work, we present a review of latest approaches that apply deep learning techniques to skin lesion classification task. In addition, some datasets used for training and validating the models are introduced, informing their characteristics and specificities, as well as popular pre-processing steps and skin lesion segmentation approaches. Finally, we comment the effectiveness of the proposed models.info:eu-repo/semantics/publishedVersio

Enhancing Skin Cancer Diagnosis with Deep Learning-Based Classification

The diagnosis of skin cancer has been identified as a significant medical challenge in the 21st century due to its complexity, cost, and subjective interpretation. Early diagnosis is critical, especially in fatal cases like melanoma, as it affects the likelihood of successful treatment. Therefore, there is a need for automated methods in early diagnosis, especially with a diverse range of image samples with varying diagnoses. An automated system for dermatological disease recognition through image analysis has been proposed and compared to conventional medical personnel-based detection. This project proposes an automated technique for skin cancer classification using images from the International Skin Imaging Collaboration (ISIC) dataset, incorporating deep learning (DL) techniques that have demonstrated significant advancements in artificial intelligence (AI) research. An automated system that recognizes and classifies skin cancer through deep learning techniques could prove useful in the medical field, as it can accurately detect the presence of skin cancer at an early stage. The ISIC dataset, which includes a vast collection of images of various skin conditions, provides an excellent opportunity to develop and validate deep learning algorithms for skin cancer classification. The proposed technique could have a significant impact on the medical industry by reducing the workload of medical personnel while providing accurate and timely diagnoses.

A novel end-to-end deep convolutional neural network based skin lesion classification framework

Background:Skin diseases are reported to contribute 1.79% of the global burden of disease. The accurate diagnosis of specific skin diseases is known to be a challenging task due, in part, to variations in skin tone, texture, body hair, etc. Classification of skin lesions using machine learning is a demanding task, due to the varying shapes, sizes, colors, and vague boundaries of some lesions. The use of deep learning for the classification of skin lesion images has been shown to help diagnose the disease at its early stages. Recent studies have demonstrated that these models perform well in skin detection tasks, with high accuracy and efficiency.Objective:Our paper proposes an end-to-end framework for skin lesion classification, and our contributions are two-fold. Firstly, two fundamentally different algorithms are proposed for segmenting and extracting features from images during image preprocessing. Secondly, we present a deep convolutional neural network model, S-MobileNet that aims to classify 7 different types of skin lesions.Methods:We used the HAM10000 dataset, which consists of 10000 dermatoscopic images from different populations and is publicly available through the International Skin Imaging Collaboration (ISIC) Archive. The image data was preprocessed to make it suitable for modeling. Exploratory data analysis (EDA) was performed to understand various attributes and their relationships within the dataset. A modified version of a Gaussian filtering algorithm and SFTA was applied for image segmentation and feature extraction. The processed dataset was then fed into the S-MobileNet model. This model was designed to be lightweight and was analysed in three dimensions: using the Relu Activation function, the Mish activation function, and applying compression at intermediary layers. In addition, an alternative approach for compressing layers in the S-MobileNet architecture was applied to ensure a lightweight model that does not compromise on performance.Results:The model was trained using several experiments and assessed using various performance measures, including, loss, accuracy, precision, and the F1-score. Our results demonstrate an improvement in model performance when applying a preprocessing technique. The Mish activation function was shown to outperform Relu. Further, the classification accuracy of the compressed S-MobileNet was shown to outperform S-MobileNet.Conclusions:To conclude, our findings have shown that our proposed deep learning-based S-MobileNet model is the optimal approach for classifying skin lesion images in the HAM10000 dataset. In the future, our approach could be adapted and applied to other datasets, and validated to develop a skin lesion framework that can be utilised in real-time