A survey, review, and future trends of skin lesion segmentation and classification

The computer-aided diagnosis (CAD) and the analysis of skin lesions using deep learning models have become common in the last decade. The proposed CAD systems have considered various datasets and deep learning models. The transfer of knowledge from particular pre-trained models to others has also gained importance due to the efficient convergence and superior results. This study presents the design and implementation of a transfer learning model using Convolutional Neural Networks (CNN) with variable training epoch numbers to classify skin lesion images obtained by smartphones. The model is divided into the inner and external CNN models to train and transfer the knowledge, and the preprocessing and data augmentation are not applied. Several experiments are performed to classify cancerous and non-cancerous skin lesions and all skin lesion types provided in the dataset separately. The designed model increased the classification rates by 20% compared to the conventional CNN. The transfer learning model achieved 0.81, 0.88, and 0.86 mean recall, mean specificity, and mean accuracy in detecting cancerous lesions, and 0.83, 0.90, and 0.86 macro recall, macro precision, and macro F1 score in classifying six skin lesions. The obtained results show the efficacy of transfer learning in skin lesion diagnosis.

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Skin Lesion Classification Using CNN-based Transfer Learning Model

The Computer-aided Diagnosis or Detection (CAD) approach for skin lesion analysis is an emerging field of research that has the potential to alleviate the burden and cost of skin cancer screening. Researchers have recently indicated increasing interest in developing such CAD systems, with the intention of providing a user-friendly tool to dermatologists in order to reduce the challenges that are raised by manual inspection. The purpose of this article is to provide a comprehensive literature review of cutting-edge CAD techniques published between 2011 and 2020. The Preferred Reporting Items for Systematic Reviews and MetaAnalyses (PRISMA) method was used to identify a total of 365 publications, 221 for skin lesion segmentation and 144 for skin lesion classification. These articles are analyzed and summarized in a number of different ways so that we can contribute vital information regarding the methods for the development of CAD systems. These ways include: relevant and essential definitions and theories, input data (datasets utilization, preprocessing, augmentations, and fixing imbalance problems), method configuration (techniques, architectures, module frameworks, and losses), training tactics (hyperparameter settings), and evaluation criteria (metrics). We also intend to investigate a variety of performance-enhancing approaches, including ensemble and post-processing. In addition, in this survey, we highlight the primary difficulties associated with evaluating skin lesion segmentation and classification systems using minimal datasets, as well as the potential solutions to these difficulties. In conclusion, enlightening findings, recommendations, and trends are discussed for the pur1 ar X iv :2 20 8. 12 23 2v 1 [ ee ss .I V ] 2 5 A ug 2 02 2 pose of future research surveillance in related fields of interest. It is anticipated that it will guide researchers of all levels, from beginners to experts, in the process of developing an automated and robust CAD system for skin lesion analysis.

Skin Lesion Analysis: A State-of-the-Art Survey, Systematic Review, and Future Trends

Green space is any green infrastructure consisting of vegetation. Green space is linked with improving mental and physical health, providing opportunities for social interactions and physical activities, and aiding the environment. The quality of green space refers to the condition of the green space. Past machine learning-based studies have emphasized that littering, lack of maintenance, and dirtiness negatively impact the perceived quality of green space. These methods assess green spaces and their qualities without considering the human perception of green spaces. Domain-based methods, on the other hand, are labour-intensive, time-consuming, and challenging to apply to large-scale areas. This research proposes to build, evaluate, and deploy a machine learning methodology for assessing the quality of green space at a human-perception level using transfer learning on pre-trained models. The results indicated that the developed models achieved high scores across six performance metrics: accuracy, precision, recall, F1-score, Cohen’s Kappa, and Average ROC-AUC. Moreover, the models were evaluated for their file size and inference time to ensure practical implementation and usage. The research also implemented Grad-CAM as means of evaluating the learning performance of the models using heat maps. The best-performing model, ResNet50, achieved 98.98% accuracy, 98.98% precision, 98.98% recall, 99.00% F1-score, a Cohen’s Kappa score of 0.98, and an Average ROC-AUC of 1.00. The ResNet50 model has a relatively moderate file size and was the second quickest to predict. Grad-CAM visualizations show that ResNet50 can precisely identify areas most important for its learning. Finally, the ResNet50 model was deployed on the Streamlit cloud-based platform as an interactive web application.

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Green Space Quality Analysis Using Machine Learning Approaches

Skin Cancer is the most common (accounting for 40% of cancer cases globally) and potentially life-threatening type of cancers. It was diagnosed in about 5.6 million individuals last year. Automated classification of skin lesions through images has been a challenge throughout the years because of fine variability in their appearance. Deep Learning techniques exhibit potential in tackling fine-margined image-based analysis and manage to provide accurate results. The three modelling stages include data collection and augmentation, model architecture and finally prediction into 7 different types of skin cancer namely actinic keratoses, basal cell carcinoma, benign keratosis-like lesions, dermatofibroma, melanoma, melanocytic nevi and vascular lesions. A Convolutional Neural Network was fabricated (using TensorFlow) obtaining an accuracy of 81.24%. Further Transfer learning Approach was implemented in PyTorch, which yielded accuracies of 96.40%, 98.20%, 98.70% and 99.04% respectively for Wide Resnet101, Resnet50, Densenet121 and VGG19 with batch normalization, which are all trained end-to-end from images directly, to proliferate the scalability of these models and curtail initial diagnostic costs. The aim of this research paper is to render non-invasive skin cancer screening a common norm, making it simpler.

Atharva Jibhakate

Papers

Skin Lesion Classification using Deep Learning and Image Processing