Universal in vivo Textural Model for Human Skin based on Optical Coherence Tomograms

Currently, diagnosis of skin diseases is based primarily on visual pattern recognition skills and expertise of the physician observing the lesion. Even though dermatologists are trained to recognize patterns of morphology, it is still a subjective visual assessment. Tools for automated pattern recognition can provide objective information to support clinical decision-making. Noninvasive skin imaging techniques provide complementary information to the clinician. In recent years, optical coherence tomography has become a powerful skin imaging technique. According to specific functional needs, skin architecture varies across different parts of the body, as do the textural characteristics in OCT images. There is, therefore, a critical need to systematically analyze OCT images from different body sites, to identify their significant qualitative and quantitative differences. Sixty-three optical and textural features extracted from OCT images of healthy and diseased skin are analyzed and in conjunction with decision-theoretic approaches used to create computational models of the diseases. We demonstrate that these models provide objective information to the clinician to assist in the diagnosis of abnormalities of cutaneous microstructure, and hence, aid in the determination of treatment. Specifically, we demonstrate the performance of this methodology on differentiating basal cell carcinoma (BCC) and squamous cell carcinoma (SCC) from healthy tissue

Computer-Aided Detection of Skin Cancer Detection from Lesion Images via Deep-Learning Techniques

More and more genetic and metabolic abnormalities are now known to cause cancer, which is typically fatal. Any particular body part may become infected by cancerous cells, which can be fatal. One of the most prevalent types of cancer is skin cancer, which is spreading worldwide.The primary subtypes of skin cancer are squamous and basal cell carcinomas, as well as melanoma, which is clinically aggressive and accounts for the majority of fatalities. Screening for skin cancer is so crucial.Deep Learning is one of the best options to quickly and precisely diagnose skin cancer (DL).This study used the Convolution Neural Network (CNN) deep learning technique to distinguish between the two primary types of cancers, malignant and benign, using the ISIC2018 dataset. The 3533 skin lesions in this dataset range from benign to malignant, and nonmelanocytic to melanocytic malignancies. The images were initially enhanced and edited using ESRGAN. The preprocessing stage involved resizing, normalising, and augmenting the images. By combining the results of numerous repetitions, the CNN approach might be used to categorise images of skin lesions. Several transfer learning models, such as Resnet50, InceptionV3, and Inception Resnet, were then used for fine-tuning. The uniqueness and contribution of this study are the preprocessing stages using ESRGAN and the testing of various models (including the intended CNN, Resnet50, InceptionV3, and Inception Resnet). Results from the model we developed matched those from the pretrained model exactly. The efficiency of the suggested strategy was proved by simulations using the ISIC 2018 skin lesion dataset. In terms of accuracy, the CNN model performed better than the Resnet50 (83.7%), InceptionV3 (85.8%), and Inception Resnet (84%) models

A Review on Skin Disease Classification and Detection Using Deep Learning Techniques

Skin cancer ranks among the most dangerous cancers. Skin cancers are commonly referred to as Melanoma. Melanoma is brought on by genetic faults or mutations on the skin, which are caused by Unrepaired Deoxyribonucleic Acid (DNA) in skin cells. It is essential to detect skin cancer in its infancy phase since it is more curable in its initial phases. Skin cancer typically progresses to other regions of the body. Owing to the disease's increased frequency, high mortality rate, and prohibitively high cost of medical treatments, early diagnosis of skin cancer signs is crucial. Due to the fact that how hazardous these disorders are, scholars have developed a number of early-detection techniques for melanoma. Lesion characteristics such as symmetry, colour, size, shape, and others are often utilised to detect skin cancer and distinguish benign skin cancer from melanoma. An in-depth investigation of deep learning techniques for melanoma's early detection is provided in this study. This study discusses the traditional feature extraction-based machine learning approaches for the segmentation and classification of skin lesions. Comparison-oriented research has been conducted to demonstrate the significance of various deep learning-based segmentation and classification approaches

Enhanced Convolutional Neural Network for Non-Small Cell Lung Cancer Classification

Lung cancer is a common type of cancer that causes death if not detectedearly enough. Doctors use computed tomography (CT) images to diagnoselung cancer. The accuracy of the diagnosis relies highly on the doctor\u27sexpertise. Recently, clinical decision support systems based on deep learningvaluable recommendations to doctors in their diagnoses. In this paper, wepresent several deep learning models to detect non-small cell lung cancer inCT images and differentiate its main subtypes namely adenocarcinoma,large cell carcinoma, and squamous cell carcinoma. We adopted standardconvolutional neural networks (CNN), visual geometry group-16 (VGG16),and VGG19. Besides, we introduce a variant of the CNN that is augmentedwith convolutional block attention modules (CBAM). CBAM aims to extractinformative features by combining cross-channel and spatial information.We also propose variants of VGG16 and VGG19 that utilize a supportvector machine (SVM) at the classification layer instead of SoftMax. Wevalidated all models in this study through extensive experiments on a CTlung cancer dataset. Experimental results show that supplementing CNNwith CBAM leads to consistent improvements over vanilla CNN. Resultsalso show that the VGG variants that use the SVM classifier outperform theoriginal VGGs by a significant margin

Dermatological Detection and Classification using Machine Learning Techniques

Dermatology is the medical field that focuses on the study and treatment of skin conditions. It is a specialized branch of medicine that encompasses both diagnostic and surgical procedures related to the skin. It is a widespread disease among them. The researchers have shows a lot of attention to the early detection of lesions. Because of their proliferation ability to other parts of the body, death rates are quite high. A system that can distinguish between benign and malignant lesions is essential because melanoma can be cured with an early and accurate diagnosis. Dermoscopic skin lesion images are first segmented using data mining techniques, to identify the area of interest of the lesion part. When compared to individual classifier algorithms, dermatology datasets benefit from the various data mining techniques and feature selection methods. The SVM provides more accurate and effective skin disease prediction in terms of accuracy, precision, and Specificity