Detection and Classification of Skin Lesions using Probability Map-based Region Growing with BA-KNN Classifier (Preprint)

BACKGROUND Melanoma is one of the most hazardous existing diseases, and is a kind of threatening pigmented skin lesion. Appropriate automated diagnosis of skin lesions and the categorization of melanoma may be exceptionally enhancing premature identification of melanomas. OBJECTIVE However, Models of categorization based on deterministic skin lesion may influence multi-dimensional nonlinear problem provokes inaccurate and ineffective categorization. This research presents a novel hybrid BA-KNN classification approach for pigmented skin lesions in dermoscopy images. METHODS In the first step, the skin lesion is preprocessed via automatic preprocessing algorithm together with a fusion hair detection and removal strategy. Also, a new probability map based region growing and optimal thresholding algorithm is integrated in this system to enhance the rate of accuracy. RESULTS Moreover, to attain better efficacy, an estimate of ABCD as well as geometric features are considered during the feature extraction to describe the malignancy of the lesion. CONCLUSIONS The evaluation of the experiment reveals the efficiency of the proposed approach on dermoscopy images with better accuracy

A Deep Learning-Based Transfer Learning Framework for the Early Detection and Classification of Dermoscopic Images of Melanoma

Purpose:Less contrast between lesions and skin, blurriness, darkened lesion images, presence of bubbles, hairs are the artifactsmakes the issue challenging in timely and accurate diagnosis of melanoma. In addition, huge similarity amid nevus lesions and melanoma pose complexity in investigating the melanoma even for the expert dermatologists. Method: In this work, a computer-aided diagnosis for melanoma detection (CAD-MD) system is designed and evaluated for the early and accurate detection of melanoma using thepotentials of machine, and deep learning-based transfer learning for the classification of pigmented skin lesions. The designed CAD-MD comprises of preprocessing, segmentation, feature extraction and classification. Experiments are conducted on dermoscopic images of PH2 and ISIC 2016 publicly available datasets using machine learning and deep learning-based transfer leaning models in twofold: first, with actual images, second, with augmented images. Results:Optimal results are obtained on augmented lesion images using machine learning and deep learning models on PH2 and ISIC-16 dataset. The performance of the CAD-MD system is evaluated using accuracy, sensitivity, specificity, dice coefficient, and jacquard Index. Conclusion:Empirical results show that using the potentials of deep learning-based transfer learning model VGG-16 has significantly outperformed all employed models with an accuracy of 99.1% on the PH2 dataset.

The Study of Usefulness of a Set of Fractal Parameters to Build Classes of Disease Units Based on Images of Pigmented Skin Lesions

Dermatoscopic images are also increasingly used to train artificial neural networks for the future to provide fully automatic diagnostic systems capable of determining the type of pigmented skin lesion. Therefore, fractal analysis was used in this study to measure the irregularity of pigmented skin lesion surfaces. This paper presents selected results from individual stages of preliminary processing of the dermatoscopic image on pigmented skin lesion, in which fractal analysis was used and referred to the effectiveness of classification by fuzzy or statistical methods. Classification of the first unsupervised stage was performed using the method of analysis of scatter graphs and the fuzzy method using the Kohonen network. The results of the Kohonen network learning process with an input vector consisting of eight elements prove that neuronal activation requires a larger learning set with greater differentiation. For the same training conditions, the final results are at a higher level and can be classified as weaker. Statistics of factor analysis were proposed, allowing for the reduction in variables, and the directions of further studies were indicated.

MALIGNANT MELANOMA AND PREGNANCY

Malignant melanoma is one of the most frequent cancers diagnosed during pregnancy. Any pigmented skin lesions that change the color should be examined by an experienced dermatologist and if suspected, should be biopsied. Recent studies showed that malignant melanoma in pregnancy has not a worse outcome compared with non-pregnant state. Diagnosis of melanoma does not require an early delivery excepted pregnant patients with poor prognosis that need more aggressive treatment. Diagnosis and treatment need to be established in specialized centers with a multidisciplinary team. Pregnancy monitoring should be performed by team consisting of an obstetrician, a neonatologist and a specialist in fetal medicine.

Evolving eponymous signs in diagnostic dermoscopy

Dermoscopy is a very useful technique devised for an earlier diagnosis of skin melanoma with a clinic-pathological correlation. Later it was found to be beneficial for the diagnosis of many other pigmented skin lesions, such as seborrheic keratosis, pigmented basal cell carcinoma, hemangioma, blue nevus, atypical nevus, and mole, which can often clinically simulate melanoma. Of late, its use in general clinical dermatology is growing with the recognition of new and specific patterns in conditions such as hair disorders, inflammatory disorders, and infections/infestation. It is still in the evolving phase and many new signs are described presently. Eponyms are used almost daily in dermatology practice. The eponyms in dermoscopy, trichoscopy, and onychoscopy are based on the imaginative capability of the authors and they have been very much successful in describing them. It becomes easier to memorize and identify the various appearances for early diagnosis and management. In this article we attempt to highlight the various dermoscopic signs described in dermatology.

Store-and-forward teledermatology based on photographs taken with a smartphone by a general practitioner in case of suspicion of melanoma: evaluation of management concordance among dermatologists (Preprint)

BACKGROUND The incidence of melanoma is increasing worldwide. The effectiveness of treatment is associated with diagnostic and therapeutic delays. In this context, teledermatology (TD), especially store-and-forward TD, is a promising technology, as it can accelerate the care process. However, several studies indicate that the efficiency and reliability of this practice in cases of pigmented skin lesions is not proven and call for further work. OBJECTIVE The main objective of this study was to evaluate management concordance among teledermatologists concerning the appropriate consultation delay when reviewing photographs of suspicious pigmented skin lesions taken with a smartphone by a general practitioner (GP). METHODS Methods: We conducted management concordance analysis among different teledermatologists with experience in TD. The teledermatologists determined the consultation delay to propose to 110 patients with suspicious pigmented skin lesions viewed online based on 241 photographs taken by GPs in a real-life context. The practitioners had to decide among 4 possibilities for care management: 1. urgent need for consultation, 2. nonurgent need for consultation, 3. no need for consultation, and 4. impossible to assess: request for additional information or photographs from the GP. "Major" discordance was defined as follows: one teledermatologist decided that the patient had to receive a consultation within 15 days, while another teledermatologist decided that it was not necessary to see the patient ("simple letter to the attending physician"). RESULTS This study revealed poor agreement among dermatologists when they assessed photographs taken by a GP using a smartphone. The interrater agreement was poor regardless of the analysis modality used. The 2-to-2 interdermatologist management concordance revealed a low Cohen's kappa (between 0.11 and 0.43). Overall agreement was low (Fleiss’ kappa 0.24). We observed major discordance regarding care management for 42 clinical cases (38% of patients). CONCLUSIONS This study is in line with recent studies calling for postponement of the development of store-and-forward TD for the management of skin cancers until additional studies have been carried out.