Advanced Bio-inspired Point of Care for Skin Cancer Early Detection

Background: The early detection of melanoma significantly affects the estimated 5-year survival rate of patients. The traditional manual visual methods used in the clinical screening suffer from clinician subjectivity, low sensitivity and specificity. Materials and Methods: Amethod based on a combined approach that employs ad-hoc customised innovative hand-crafted image features together with a feed-forward Levenberg-Marquardt Neural Network System was tested on a dataset of 250 well classified dermoscopic skin lesion images. Results: This “Point of Care” System was able to reliably predict the correct classification of dermoscopic skin lesion imageswith sensibility and specificity of 97 and 95%, respectively, giving appropriate indications for their management and follow-up. Conclusion: Analysis of skin lesions by automated classification using computational methods may allow medical practitioners and patients to proactively track skin lesions and detect cancer earlier. Introduction Finding adequate and cost-effective clinical protocols forthe early diagnosis of cancer is one of the major challenging tasks in oncology, especially for skin cancer. Skin cancer is the most common human malignancy [1,2]. Melanoma represents fewer than 5% of all skin cancersbut accounts for approximately 75% of all skin-cancer-related deaths, and in the United States is responsible for over 10,000 deaths annually [2]. The early detection, or the medical discrimination, of primary skin lesions (nevi), is crucial since the estimated 5year survival rate for melanoma dropsfrom over 99% at the earliest stages to about 14% at the advanced stages [2]. The traditional visual methodused in the clinical screening for the early detection of melanoma relies upon visual analysisof the skin lesion based on the so-called “ABCDE” (Asymmetry, Border irregularity, Color variegation, Diameter, Evolution) criteria,followed potentially by dermoscopic analysis, a biopsy and histopathological examination. However, this manual visual assessment strategy suffers from clinician subjectivity, low sensitivity and specificity, since it relies only on superficial, classical features that can SL Clinical and Medical Oncology Advanced Bio-inspired Point of Care for Skin Cancer Early Detection. SL Clin Med Oncol. 2017; 1(1):111. significantly affect the characterization of melanoma both in the screening and in the follow-up of suspected skin lesion phases. Automated classification of skinlesions using images is a challenging task owing to the fine-grainedvariability in the appearance of skin lesions [3]. However, a computational method mayallow medical practitioners and patients to proactively track skinlesions and detect cancer earlier. Materials and Methods The proposed method is a bio-inspired feed-forward automatic pipeline based on morphological analysis and evaluation of the image of skin lesion obtained by dermoscopy. Preliminary segmentation and preprocessing of the dermoscopic image by State Controlled-Cellular Neural Networks (SC-CNN) [4] is performed to obtain ad-hoc gray-level of the skin lesion image and applying to this image innovative mathematical processing by novel hand-crafted image features for related cancer risk assessment. At the end, pre-trained Levenberg-Marquardt Neural Network (LMNN) [5] is used to perform ad-hoc clustering of these hand-crafted image features to obtain an efficient nevus discrimination (i.e. benign against melanoma) as well as a numerical array that is useful for the definition of the timing for the follow-up.Thus, a combined approach that employs ad-hoc customised innovative hand-crafted image features together with a feed-forward Levenberg-Marquardt Neural Network System (NNS) is proposed. This method could be easily implemented as a simple portable embedded system by introducing a “Point of Care” hardware solution based on the microcontroller device STM32 Microcontroller Unit (MCU) [6] for high-speed nevus discrimination. Figure 1 shows the proposed medical system and the related “Point of Care” for the early diagnosis of melanoma. The proposed “Point of Care” will perform automatic processing of the skin lesion image obtained by dermoscopyto discriminate a benign nevus from a suspected or malignant ones. An overview of the mathematical model inside the “Point of Care” is described in Figure 2. The method was tested in a set of 250 well classified dermoscopic skin lesion images [7]. Results and Discussion Some examples of dermoscopic skin lesions analysed by the “Point of Care” System along with related indications for their management and follow-up are reported in Table 1. The sensibility and specificity of the proposed method are reported in Table 2 and compared with those deriving from the other main automatic skin lesion analysis pipeline. Recent methods for melanoma detection use image features, neuro-fuzzy or clustering (K-means, Support Vector Machine (SVM), etc.), approaches [8]. In some cases, they are based on the study of melanocytes distribution in the skin on histopathologic images and the probabilistic analysis as per Bayesian Classifier. Some promising methods use neural networks to classify skin lesions [9], or a method based on the adaptive thresholding analysis [10]. Further methods, with acceptable results, are based on pattern analysis combined with classical statistical dermoscopy image features. However, the methods reporting high accuracy have been validated with a few number of skin images, whilst those tested with a consistent number of images exhibit lower accuracy, sensibility and specificity [8]. Several methods were proposed for the classification of skin cancer lesions based on the use of global and local features combined with different classification systems such as the SVM, the classical Artificial Neural Networks (ANN), K-nearest and Naive-Bayes algorithm. In particular, one method combined some hand-crafted image features with a deep-learning algorithm for handling other image features [8-14]. These methods used a score-based approach to perform classification of the image features. However, limited sensibility and specificity were reported with the datasetsof images analysed [7-14]. The method proposed herein showed promising results in terms of sensibility and specificity that need to be confirmed in a prospective study. Furthermore, we are going to extend the proposed method by using a specific photo-multipliersensor