Common Pigmented Skin Lesions Research Articles

Categorization of Common Pigmented Skin Lesions (CPSL) using Multi-Deep Features and Support Vector Machine.

The skin is the main organ. It is approximately 8 pounds for the average adult. Our skin is a truly wonderful organ. It isolates us and shields our bodies from hazards. However, the skin is also vulnerable to damage and distracted from its original appearance: brown, black, or blue, or combinations of those colors, known as pigmented skin lesions. These common pigmented skin lesions (CPSL) are the leading factor of skin cancer, or can say these are the primary causes of skin cancer. In the healthcare sector, the categorization of CPSL is the main problem because of inaccurate outputs, overfitting, and higher computational costs. Hence, we proposed a classification model based on multi-deep feature and support vector machine (SVM) for the classification of CPSL. The proposed system comprises two phases: First, evaluate the 11 CNN model's performance in the deep feature extraction approach with SVM, and then, concatenate the top performed three CNN model's deep features and with the help of SVM to categorize the CPSL. In the second step, 8192 and 12,288 features are obtained by combining binary and triple networks of 4096 features from the top performed CNN model. These features are also given to the SVM classifiers. The SVM results are also evaluated with principal component analysis (PCA) algorithm to the combined feature of 8192 and 12,288. The highest results are obtained with 12,288 features. The experimentation results, the combination of the deep feature of Alexnet, VGG16 and VGG19, achieved the highest accuracy of 91.7% using SVM classifier. As a result, the results show that the proposed methods are a useful tool for CPSL classification.

The Dermoscopic Patterns in Common Pigmented Skin Lesions

Objective: To determine the dermoscopic patterns in common pigmented skin lesions.
 Setting and duration Study: This study was conducted at Dermatology Department, Civil Hospital, Hyderabad, during October 18, 2020 to April 17, 2021.
 Materials and Methods: After taking approval from hospital ethical committee and written informed consent from 150 patients, all patients with pigmented skin lesions were enrolled. Demographic information were also recorded. Detailed history and thorough dermatological, physical and systemic examination was conducted. Routine investigations and histopathology was taken wherever necessary to aid the clinical diagnosis. Dermoscopic and clinical photographs were taken after informed consent. All dermoscopic findings were studied using handheld pocket dermoscope. In the case of patient having multiple lesions, only a single active lesion was selected for dermoscopy.
 Results: Age range in this study was from 15 to 60 years with median (IQR) age was17 (15 -60) years and mean duration of disease was 5.67+ 2.14 months. Among the common pigmented skin lesions, 86 (57.4%) had sharp demarcation, 29 (19.3%) had Dots and gobules, 7 (4.5%) had Pseudonetwork, 12 (8%) had Homogenous structure less pigmentation, 78 (52%) had Milia-like cysts and 21 (14%) had Cerebriform pattern.
 Conclusion: This study shows the high frequency of dermoscopic patterns in common pigmented skin lesions. This concludes that derrmoscopy is a useful and essential technique to clinically manage pigmented skin lesions, and it plays a fundamental role in early identification of pigmented lesions.

Deep Neural Frameworks Improve the Accuracy of General Practitioners in the Classification of Pigmented Skin Lesions.

This study evaluated whether deep learning frameworks trained in large datasets can help non-dermatologist physicians improve their accuracy in categorizing the seven most common pigmented skin lesions. Open-source skin images were downloaded from the International Skin Imaging Collaboration (ISIC) archive. Different deep neural networks (DNNs) (n = 8) were trained based on a random dataset constituted of 8015 images. A test set of 2003 images was used to assess the classifiers’ performance at low (300 × 224 RGB) and high (600 × 450 RGB) image resolution and aggregated data (age, sex and lesion localization). We also organized two different contests to compare the DNN performance to that of general practitioners by means of unassisted image observation. Both at low and high image resolution, the DNN framework differentiated dermatological images with appreciable performance. In all cases, the accuracy was improved when adding clinical data to the framework. Finally, the least accurate DNN outperformed general practitioners. The physician’s accuracy was statistically improved when allowed to use the output of this algorithmic framework as guidance. DNNs are proven to be high performers as skin lesion classifiers and can improve general practitioner diagnosis accuracy in a routine clinical scenario.

The HAM10000 dataset, a large collection of multi-source dermatoscopic images of common pigmented skin lesions

Training of neural networks for automated diagnosis of pigmented skin lesions is hampered by the small size and lack of diversity of available datasets of dermatoscopic images. We tackle this problem by releasing the HAM10000 (“Human Against Machine with 10000 training images”) dataset. We collected dermatoscopic images from different populations acquired and stored by different modalities. Given this diversity we had to apply different acquisition and cleaning methods and developed semi-automatic workflows utilizing specifically trained neural networks. The final dataset consists of 10015 dermatoscopic images which are released as a training set for academic machine learning purposes and are publicly available through the ISIC archive. This benchmark dataset can be used for machine learning and for comparisons with human experts. Cases include a representative collection of all important diagnostic categories in the realm of pigmented lesions. More than 50% of lesions have been confirmed by pathology, while the ground truth for the rest of the cases was either follow-up, expert consensus, or confirmation by in-vivo confocal microscopy.

Differentiation of Benign Pigmented Skin Lesions with the Aid of Computer Image Analysis: A Novel Approach

BackgroundThe differential diagnosis of common pigmented skin lesions is important in cosmetic dermatology. The computer aided image analysis would be a potent ancillary diagnostic tool when patients are hesitant to undergo a skin biopsy.ObjectiveWe investigated the numerical parameters discriminating each pigmented skin lesion from another with statistical significance.MethodsFor each of the five magnified digital images containing clinically diagnosed nevus, lentigo and seborrheic keratosis, a total of 23 parameters describing the morphological, color, texture and topological features were calculated with the aid of a self-developed image analysis software. A novel concept of concentricity was proposed, which represents how closely the color segmentation resembles a concentric circle.ResultsMorphologically, seborrheic keratosis was bigger and spikier than nevus and lentigo. The color histogram revealed that nevus was the darkest and had the widest variation in tone. In the aspect of texture, the surface of the nevus showed the highest contrast and correlation. Finally, the color segmented pattern of the nevus and lentigo was far more concentric than that of seborrheic keratosis.ConclusionWe found that the subtle distinctions between nevus, lentigo and seborrheic keratosis, which are likely to be unrecognized by ocular inspection, are well emphasized and detected with the aid of software.

Tumour thickness in malignant melanoma: does rapid referral help?

The prognosis of patients with melanoma can be improved by early diagnosis and excision. In 1981 a ‘Pigmented Lesion Clinic’ was started in Southampton to ensure that all patients who went to their general practitioner with a possible melanoma were seen in the dermatology department within 1 week. Before the clinic started an evening lecture was given to local general practitioners and all were sent a booklet containing colour photographs of the common pigmented skin lesions. During the following 4 years 92 of the 1530 patients seen in this clinic had a malignant melanoma. Sixty‐three per cent of the patients with malignant melanoma had skin type 1 or II and 62% blue or green eyes. Nineteen per cent had lived in Asia, Africa or Australia for more than I year. There was no correlation between the length of history as given by the patient and tumour thickness. Nor were bleeding or itching, present in 47% and 35% of patients respectively, factors which speeded up the patients presentation to their general practitioners. The mean thickness of the melanomas from patients attending the ‘Pigmented Lesion Clinic’ was not significantly different to that of the 42 patients with malignant melanomas referred to other specialists in Southampton not offering a rapid referral service—2.7 mm (± 2.3 mm S.D.) and 2.3 mm (± 2.9 mm S.D.) respectively. The results suggest that education of general practitioners without education of the general public did not improve the prognosis of patients with malignant melanoma in spite of easy access to hospital.