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The study aims to cope with the problems confronted in the skin lesion datasets with less training data toward the classification of melanoma. The vital, challenging issue is the insufficiency of training data that occurred while classifying the lesions as melanoma and non-melanoma.

In this work, a transfer learning (TL) framework Transfer Constituent Support Vector Machine (TrCSVM) is designed for melanoma classification based on feature-based domain adaptation (FBDA) leveraging the support vector machine (SVM) and Transfer AdaBoost (TrAdaBoost). The working of the framework is twofold: at first, SVM is utilized for domain adaptation for learning much transferrable representation between source and target domain. In the first phase, for homogeneous domain adaptation, it augments features by transforming the data from source and target (different but related) domains in a shared-subspace. In the second phase, for heterogeneous domain adaptation, it leverages knowledge by augmenting features from source to target (different and not related) domains to a shared-subspace. Second, TrAdaBoost is utilized to adjust the weights of wrongly classified data in the newly generated source and target datasets.

The experimental results empirically prove the superiority of TrCSVM than the state-of-the-art TL methods on less-sized datasets with an accuracy of 98.82%.

Experiments are conducted on six skin lesion datasets and performance is compared based on accuracy, precision, sensitivity, and specificity. The effectiveness of TrCSVM is evaluated on ten other datasets towards testing its generalizing behavior. Its performance is also compared with two existing TL frameworks (TrResampling, TrAdaBoost) for the classification of melanoma.

The purpose of this paper is to describe innovative machine vision methods that have been employed for the capture and analysis of 3D skin textures; and the resulting potential for assisting with identification of suspicious lesions in the detection of skin cancer.

A machine vision approach has been employed for analysis of 3D skin textures. This involves an innovative application of photometric stereo for the capture of the textures, and a range of methods for analysing and quantifying them, including statistical methods and neural networks.

3D skin texture has been identified as a useful indicator of skin cancer. It can be used to improve realism of virtual skin reconstructions in tele‐dermatology. 3D texture features can also be combined with 2D features to obtain a more robust classifier for improving diagnostic accuracy, thereby assisting with the long‐term goal of implementing computer‐aided diagnostics for skin cancer.

The device developed for capturing 3D skin textures is known as the “Skin Analyser”, and as far as the authors know it is unique in the world in being able to recover 3D textures from pigmented lesions in vivo. There currently exist numerous methods for analysing lesions, including manual inspection (using established heuristics commonly known as ABCD rules), dermoscopy and SIAoscopy. The ability to capture and analyse 3D lesion textures complements these existing techniques and forms a valuable additional indicator for assisting with the early detection of dangerous skin cancers such as melanoma.

Objective and study design: to assess quality of a quick and early diagnosis route (QED) by determining effectiveness and cost‐ effectiveness of five clinics compared with three conventional outpatient clinics. Prospective economic evaluation. Six‐month cohort of all referrals (November 1996‐April 1997). Subjects: all referrals for suspected cancers of: upper gastro‐intestinal tract; urinary tract, prostate and testis; skin. Effectiveness: median days saved between GP referral and date of: diagnostic appointment; consultant decision; intervention. Results: GP referral to diagnostic appointment: QED was effective (median days) for all clinics. Diagnostic appointment to consultant decision: QED was effective for testicular and haematuria clinics. Consultant decision to intervention: QED was effective for haematuria, testicular and melanoma clinics. Cost‐effectiveness: extra (incremental) NHS cost per patient diagnosed. Results: Less than £5 per day saved between GP referral and diagnostic appointment for: endoscopy; haematuria; prostate; testicular; melanoma. Less than £3 per day saved between GP referral and consultant decision for: testicular; haematuria. Less than £3 per day saved between GP referral and intervention for: endoscopy; haematuria; testicular; melanoma. Conclusion: A “quick and early” diagnostic route provides a higher quality service through improved effectiveness and cost‐effectiveness compared to conventional outpatients.

The aim of this paper is to distinguish pedagogies supporting critical health literacy development in adolescent populations. Specifically, for sun safety education in schools.

The paper draws on an exploratory intrinsic case study design to qualitatively examine the learning conditions that Pre-Service Teachers' (PsTs) mobilise to advance Health Literary (HL) in learning activities.

This paper presents data that shows the different ways thirty Pre-Service Teachers (PsTs) in Western Australia conceptualise HL in sun safety education for Year 7 students (12–13 years old). Examination of three consecutive lesson plans categorised learning activities (n = 444) according to HL competencies. Data shows that the PsTs pedagogically advance HL but are constrained when conceptualising learning to support critical HL. Further examination of the lesson plans of the 11 PsTs who pedagogically advanced learning to support a critical level of health literacy, distinguished the learning conditions and pedagogies supporting critically health literate adolescents.

By distinguishing pedagogies to situate individual and social health within broader societal goals, the paper identifies teacher education institutions as key players enabling young people to socially advocate healthier living, particularly, regarding melanoma and non-melanoma incidence.

The mortality rate due to skin cancers has been increasing over the past decades. Early detection and treatment of skin cancers can save lives. However, due to visual resemblance of normal skin and lesion and blurred lesion borders, skin cancer diagnosis has become a challenging task even for skilled dermatologists. Hence, the purpose of this study is to present an image-based automatic approach for multiclass skin lesion classification and compare the performance of various models.

In this paper, the authors have presented a multiclass skin lesion classification approach based on transfer learning of deep convolutional neural network. The following pre-trained models have been used: VGG16, VGG19, ResNet50, ResNet101, ResNet152, Xception, MobileNet and compared their performances on skin cancer classification.

The experiments have been performed on HAM10000 dataset, which contains 10,015 dermoscopic images of seven skin lesion classes. The categorical accuracy of 83.69%, Top2 accuracy of 91.48% and Top3 accuracy of 96.19% has been obtained.

Early detection and treatment of skin cancer can save millions of lives. This work demonstrates that the transfer learning can be an effective way to classify skin cancer images, providing adequate performance with less computational complexity.

Observes that a major risk factor for developing malignant melanoma is severe exposure to the sun in childhood and adolescence. Summarises the epidemiology of skin cancers, including the alarming rate of increase in new cases of malignant melanoma. Describes the Government target of halting the rise each year in newly diagnosed cases of skin cancers by 2005. Given the dearth of suitable resources available to assist health educators in teaching young people about the harmful effects of the sun, describes the development of a user‐friendly teaching pack suitable for Key Stage 2 children (7 to 11 years), which aims to promote positive behaviours. Suggests that educating children at this crucial stage may lead to a decrease in new cases of skin cancer in future years. Shows that early evaluation of the teaching course has so far been encouraging.

Automated skin lesion analysis plays a vital role in early detection. Having relatively small-sized imbalanced skin lesion datasets impedes learning and dominates research in automated skin lesion analysis. The unavailability of adequate data poses difficulty in developing classification methods due to the skewed class distribution.

Boosting-based transfer learning (TL) paradigms like Transfer AdaBoost algorithm can compensate for such a lack of samples by taking advantage of auxiliary data. However, in such methods, beneficial source instances representing the target have a fast and stochastic weight convergence, which results in “weight-drift” that negates transfer. In this paper, a framework is designed utilizing the “Rare-Transfer” (RT), a boosting-based TL algorithm, that prevents “weight-drift” and simultaneously addresses absolute-rarity in skin lesion datasets. RT prevents the weights of source samples from quick convergence. It addresses absolute-rarity using an instance transfer approach incorporating the best-fit set of auxiliary examples, which improves balanced error minimization. It compensates for class unbalance and scarcity of training samples in absolute-rarity simultaneously for inducing balanced error optimization.

Promising results are obtained utilizing the RT compared with state-of-the-art techniques on absolute-rare skin lesion datasets with an accuracy of 92.5%. Wilcoxon signed-rank test examines significant differences amid the proposed RT algorithm and conventional algorithms used in the experiment.

Experimentation is performed on absolute-rare four skin lesion datasets, and the effectiveness of RT is assessed based on accuracy, sensitivity, specificity and area under curve. The performance is compared with an existing ensemble and boosting-based TL methods.

This paper aims to investigate in a self-designed closed loop reactor process conditions for thermal inactivation of B16 melanoma cells by microwave and conventional heating.

Besides control experiments (37°C), inactivation rate was determined in the range from 42°C to 46°C. Heating was achieved either by microwave radiation at 2.45 GHz or by warm water. To distinguish viable from dead cells, AnnexinV staining method was used and supported by field effect scanning electron microscopy (FE-SEM) imaging. Furthermore, numerical simulations were done to get a closer look into both heating devices. To investigate the thermal influence on cell inactivation and the differences between heating methods, a reaction kinetics approach was added as well.

Control experiments and heating at 42°C resulted in low inactivation rates. Inactivation rate at 44°C remained below 12% under conventional, whereas it increased to >70% under microwave heating. At 46°C, inactivation rate attained 68% under conventional heating; meanwhile, even 88% were determined under microwave heating. FE-SEM images showed a porous membrane structure under microwave heating in contrast to mostly intact conventional heated cells. Numerical simulations of both heating devices and a macroscopic Arrhenius approach could not sufficiently explain the observed differences in inactivation.

A combination of thermal and electrical effects owing to microwave heating results in higher inactivation rates than conventional heating achieves. Nevertheless, it was not possible to determine the exact mechanisms of inactivation under microwave radiation.

The flux residues on almost all soldered printed circuit boards are removed using the chlorofluorocarbon (CFC) 113. In just one year's time production of this solvent will almost certainly be curtailed, on a scale agreed internationally. This is a major issue that needs to be addressed urgently by the electronics assembly industry worldwide. This paper presents (i) the background that has led to the restrictions being placed on production and consumption of solvent 113, (ii) the international agreement and timetable for the implementation of the restrictions and (iii) the perceived opportunities that are available to the electronics assembly industry to meet this challenge.

Computed tomography (CT) scan can provide valuable information in the diagnosis of lung diseases. To detect the location of the cancerous lung nodules, this work uses novel deep learning methods. The majority of the early investigations used CT, magnetic resonance and mammography imaging. Using appropriate procedures, the professional doctor in this sector analyses these images to discover and diagnose the various degrees of lung cancer. All of the methods used to discover and detect cancer illnesses are time-consuming, expensive and stressful for the patients. To address all of these issues, appropriate deep learning approaches for analyzing these medical images, which included CT scan images, were utilized.

Radiologists currently employ chest CT scans to detect lung cancer at an early stage. In certain situations, radiologists' perception plays a critical role in identifying lung melanoma which is incorrectly detected. Deep learning is a new, capable and influential approach for predicting medical images. In this paper, the authors employed deep transfer learning algorithms for intelligent classification of lung nodules. Convolutional neural networks (VGG16, VGG19, MobileNet and DenseNet169) are used to constrain the input and output layers of a chest CT scan image dataset.

The collection includes normal chest CT scan pictures as well as images from two kinds of lung cancer, squamous and adenocarcinoma impacted chest CT scan images. According to the confusion matrix results, the VGG16 transfer learning technique has the highest accuracy in lung cancer classification with 91.28% accuracy, followed by VGG19 with 89.39%, MobileNet with 85.60% and DenseNet169 with 83.71% accuracy, which is analyzed using Google Collaborator.

The proposed approach using VGG16 maximizes the classification accuracy when compared to VGG19, MobileNet and DenseNet169. The results are validated by computing the confusion matrix for each network type.