Deep learning applied to the histopathological diagnosis of ameloblastomas and ameloblastic carcinomas.

BACKGROUND: Odontogenic tumors (OT) are composed of heterogeneous lesions, which can be benign or malignant, with different behavior and histology. Within this classification, ameloblastoma and ameloblastic carcinoma (AC) represent a diagnostic challenge in daily histopathological practice due to their similar characteristics and the limitations that incisional biopsies represent. From these premises, we wanted to test the usefulness of models based on artificial intelligence (AI) in the field of oral and maxillofacial pathology for differential diagnosis. The main advantages of integrating Machine Learning (ML) with microscopic and radiographic imaging is the ability to significantly reduce intra-and inter observer variability and improve diagnostic objectivity and reproducibility. METHODS: Thirty Digitized slides were collected from different diagnostic centers of oral pathology in Brazil. After performing manual annotation in the region of interest, the images were segmented and fragmented into small patches. In the supervised learning methodology for image classification, three models (ResNet50, DenseNet, and VGG16) were focus of investigation to provide the probability of an image being classified as class0 (i.e., ameloblastoma) or class1 (i.e., Ameloblastic carcinoma). RESULTS: The training and validation metrics did not show convergence, characterizing overfitting. However, the test results were satisfactory, with an average for ResNet50 of 0.75, 0.71, 0.84, 0.65, and 0.77 for accuracy, precision, sensitivity, specificity, and F1-score, respectively. CONCLUSIONS: The models demonstrated a strong potential of learning, but lack of generalization ability. The models learn fast, reaching a training accuracy of 98%. The evaluation process showed instability in validation; however, acceptable performance in the testing process, which may be due to the small data set. This first investigation opens an opportunity for expanding collaboration to incorporate more complementary data; as well as, developing and evaluating new alternative models.

The use of deep learning state-of-the-art architectures for oral epithelial dysplasia grading: A comparative appraisal.

BACKGROUND: Dysplasia grading systems for oral epithelial dysplasia are a source of disagreement among pathologists. Therefore, machine learning approaches are being developed to mitigate this issue. METHODS: This cross-sectional study included a cohort of 82 patients with oral potentially malignant disorders and correspondent 98 hematoxylin and eosin-stained whole slide images with biopsied-proven dysplasia. All whole-slide images were manually annotated based on the binary system for oral epithelial dysplasia. The annotated regions of interest were segmented and fragmented into small patches and non-randomly sampled into training/validation and test subsets. The training/validation data were color augmented, resulting in a total of 81,786 patches for training. The held-out independent test set enrolled a total of 4,486 patches. Seven state-of-the-art convolutional neural networks were trained, validated, and tested with the same dataset. RESULTS: The models presented a high learning rate, yet very low generalization potential. At the model development, VGG16 performed the best, but with massive overfitting. In the test set, VGG16 presented the best accuracy, sensitivity, specificity, and area under the curve (62%, 62%, 66%, and 65%, respectively), associated with the higher loss among all Convolutional Neural Networks (CNNs) tested. EfficientB0 has comparable metrics and the lowest loss among all convolutional neural networks, being a great candidate for further studies. CONCLUSION: The models were not able to generalize enough to be applied in real-life datasets due to an overlapping of features between the two classes (i.e., high risk and low risk of malignization).

Automatic Classification of Emotions Based on Cardiac Signals: A Systematic Literature Review.

Emotions play a pivotal role in human cognition, exerting influence across diverse domains of individuals' lives. The widespread adoption of artificial intelligence and machine learning has spurred interest in systems capable of automatically recognizing and classifying emotions and affective states. However, the accurate identification of human emotions remains a formidable challenge, as they are influenced by various factors and accompanied by physiological changes. Numerous solutions have emerged to enable emotion recognition, leveraging the characterization of biological signals, including the utilization of cardiac signals acquired from low-cost and wearable sensors. The objective of this work was to comprehensively investigate the current trends in the field by conducting a Systematic Literature Review (SLR) that focuses specifically on the detection, recognition, and classification of emotions based on cardiac signals, to gain insights into the prevailing techniques employed for signal acquisition, the extracted features, the elicitation process, and the classification methods employed in these studies. A SLR was conducted using four research databases, and articles were assessed concerning the proposed research questions. Twenty seven articles met the selection criteria and were assessed for the feasibility of using cardiac signals, acquired from low-cost and wearable devices, for emotion recognition. Several emotional elicitation methods were found in the literature, including the algorithms applied for automatic classification, as well as the key challenges associated with emotion recognition relying solely on cardiac signals. This study extends the current body of knowledge and enables future research by providing insights into suitable techniques for designing automatic emotion recognition applications. It emphasizes the importance of utilizing low-cost, wearable, and unobtrusive devices to acquire cardiac signals for accurate and accessible emotion recognition.

Machine learning for the prediction of toxicities from head and neck cancer treatment: A systematic review with meta-analysis.

INTRODUCTION: The aim of the present systematic review (SR) is to summarize Machine Learning (ML) models currently used to predict head and neck cancer (HNC) treatment-related toxicities, and to understand the impact of image biomarkers (IBMs) in prediction models (PMs). The present SR was conducted following the guidelines of the PRISMA 2022 and registered in PROSPERO database (CRD42020219304). METHODS: The acronym PICOS was used to develop the focused review question (Can PMs accurately predict HNC treatment toxicities?) and the eligibility criteria. The inclusion criteria enrolled Prediction Model Studies (PMSs) with patient cohorts that were treated for HNC and developed toxicities. Electronic database search encompassed PubMed, EMBASE, Scopus, Cochrane Library, Web of Science, LILACS, and Gray Literature (Google Scholar and ProQuest). Risk of Bias (RoB) was assessed through PROBAST and the results were synthesized based on the data format (with and without IBMs) to allow comparison. RESULTS: A total of 28 studies and 4,713 patients were included. Xerostomia was the most frequently investigated toxicity (17; 60.71 %). Sixteen (57.14 %) studies reported using radiomics features in combination with clinical or dosimetrics/dosiomics for modelling. High RoB was identified in 23 studies. Meta-analysis (MA) showed an area under the receiver operating characteristics curve (AUROC) of 0.82 for models with IBMs and 0.81 for models without IBMs (p value < 0.001), demonstrating no difference among IBM- and non-IBM-based models. DISCUSSION: The development of a PM based on sample-specific features represents patient selection bias and may affect a model's performance. Heterogeneity of the studies as well as non-standardized metrics prevent proper comparison of studies, and the absence of an independent/external test does not allow the evaluation of the model's generalization ability. CONCLUSION: IBM-featured PMs are not superior to PMs based on non-IBM predictors. The evidence was appraised as of low certainty.

Machine learning concepts applied to oral pathology and oral medicine: A convolutional neural networks' approach.

INTRODUCTION: Artificial intelligence models and networks can learn and process dense information in a short time, leading to an efficient, objective, and accurate clinical and histopathological analysis, which can be useful to improve treatment modalities and prognostic outcomes. This paper targets oral pathologists, oral medicinists, and head and neck surgeons to provide them with a theoretical and conceptual foundation of artificial intelligence-based diagnostic approaches, with a special focus on convolutional neural networks, the state-of-the-art in artificial intelligence and deep learning. METHODS: The authors conducted a literature review, and the convolutional neural network's conceptual foundations and functionality were illustrated based on a unique interdisciplinary point of view. CONCLUSION: The development of artificial intelligence-based models and computer vision methods for pattern recognition in clinical and histopathological image analysis of head and neck cancer has the potential to aid diagnosis and prognostic prediction.

The value of artificial intelligence for detection and grading of prostate cancer in human prostatectomy specimens: a validation study.

BACKGROUND: The Gleason grading system is an important clinical practice for diagnosing prostate cancer in pathology images. However, this analysis results in significant variability among pathologists, hence creating possible negative clinical impacts. Artificial intelligence methods can be an important support for the pathologist, improving Gleason grade classifications. Consequently, our purpose is to construct and evaluate the potential of a Convolutional Neural Network (CNN) to classify Gleason patterns. METHODS: The methodology included 6982 image patches with cancer, extracted from radical prostatectomy specimens previously analyzed by an expert uropathologist. A CNN was constructed to accurately classify the corresponding Gleason. The evaluation was carried out by computing the corresponding 3 classes confusion matrix; thus, calculating the percentage of precision, sensitivity, and specificity, as well as the overall accuracy. Additionally, k-fold three-way cross-validation was performed to enhance evaluation, allowing better interpretation and avoiding possible bias. RESULTS: The overall accuracy reached 98% for the training and validation stage, and 94% for the test phase. Considering the test samples, the true positive ratio between pathologist and computer method was 85%, 93%, and 96% for specific Gleason patterns. Finally, precision, sensitivity, and specificity reached values up to 97%. CONCLUSION: The CNN model presented and evaluated has shown high accuracy for specifically pattern neighbors and critical Gleason patterns. The outcomes are in line and complement others in the literature. The promising results surpassed current inter-pathologist congruence in classical reports, evidencing the potential of this novel technology in daily clinical aspects.

Impacts of decentralization in health systems in the state of São Paulo, Brazil.

OBJECTIVE: To evaluate a p-median model for health care services accessibility based on decentralization and optimal allocation of Primary Health Care Units in the State of São Paulo, Brazil. METHODS: Using geographical data of Primary Health Care Units located in the State of São Paulo, potential support and supply facility allocations were simulated by means of a random approach. Several constraints were then imposed on the system to simulate different scenarios. Results were assessed according to geographic disposition. RESULTS: Using a fixed number of supply facilities, ten as a constraint, the p-median approach allocated three facilities near the state capital (the area with the highest concentration of Primary Health Care Units), while remaining facilities were spread throughout the west of the state. A second round of tests assessed the impact of fixed costs alone on optimization, ranging from 71 optimal locations with a fixed unit cost to six optimal locations at a cost 300-fold higher. This finding was relevant to decision-making, since it encompassed scenarios in which only the final number of facilities or only the budget was known. A third set of simulations contemplates an intermediate scenario. CONCLUSION: The p-median approach was capable of optimizing a wide range of scenarios with an average running time of less than 2 hours and 30 minutes while considering a large dataset of more than 4,000 locations. In spite of some shortcomings, such as estimation of Euclidean distances, the method is simple yet powerful enough to be considered a useful tool to assist decision makers in the distribution of resources, and facilities across large areas with high number of locations to be supplied.

Radial-Biased Tracking Method to Assess Tissue Displacement in Intravascular Ultrasound Sequences: A Phantom Framework Evaluation.

OBJECTIVES: We created and evaluated a pixel-tracking method capable of accurately identify the displacement of tissue in intravascular ultrasound (IVUS) images. METHODS: Our proposed pixel-tracking method assessed the horizontal and vertical displacement of tissue from a numerical phantom of IVUS sequences. The proposed tracking method is based on a block-matching framework, comparing 2 distinct frames within a selected region by normalized cross-correlation. Our method, specialized for IVUS applications, reduced the tracking area by implementing a limiting radius and a radial bias during the search. RESULTS: The method was evaluated by using 54 numerical phantom image sequences from 9 distinct arterial models, resulting in different arteries with atherosclerotic plaques under a range of pressures. The ground truth reference coordinates of the tracked tissue were extracted from each numerical phantom sequence. Our results were compared to 8 other methods present in the literature. The mean absolute tracking errors ± SD for our method were 15.56 ± 19.46 and 13.04 ± 13.82 µm for the horizontal and vertical directions, respectively, between 2 subsequent frames, and 162.58 ± 305.93 and 102.22 ± 130.61 µm from lower to higher pressures in the range of 6 frames (n = 42,036). CONCLUSIONS: Our application-specific pixel-tracking method showed promising results and no statistically significant tracking error (P = .954), comparable to state-of-the-art methods present in the literature. Application-specific tracking methods have advantages over general methods by turning tissue-specific behavior into a directional bias in the tracking algorithm.

Robust pulmonary segmentation for chest radiography, combining enhancement, adaptive morphology and innovative active contours

Abstract Introduction Statistical data reveal that approximately 140 million radiological exams are performed annually in Brazil. These exams are designed to detect and to analyze fractures, caused by different types of trauma; as well as, to diagnose pathologies such as pulmonary diseases. For better visualization of those lesions or abnormalities, methods of image segmentation can be implemented. Such methods lead to the separation of the region of interest, which allows extracting the characteristics and anomalies of the desired tissue. However, the methods developed by researchers in this area still have restrictions. Consequently, we present an automatic pulmonary segmentation approach that overcomes these constraints. Methods This method is composed of a combination of Discrete Wavelet Packet Frame (DWPF), morphological operations and Gradient Vector Flow (GVF). The methodology is divided into four steps: Pre-processing - the original image is enhanced by discrete wavelet; Processing - where occurs a combination of the Otsu threshold with a series of morphological operations in order to identify the pulmonary object; Post-processing - an innovative form of using GVF improves the binary information of pulmonary tissue, and; Evaluation - the segmented images were evaluated for accuracy of detection the pulmonary region and border. Results The evaluation was carried out by segmenting 247 digital X-ray challenging images of the thorax human. The results show high for values of Overlap (97,63% ± 3.34%), and Average Contour Distance (0.69mm ± 0.95mm). Conclusion The results allow verifying that the proposed technique is robust and more accurate than other methods of lung segmentation, besides being a fully automatic method of lung segmentation.

Realistic deformable 3D numeric phantom for transcutaneous ultrasound

Abstract Introduction Numerical phantoms are important tools to design, calibrate and evaluate several methods in various image-processing applications, such as echocardiography and mammography. We present a framework for creating ultrasound numerical deformable phantoms based on Finite Element Method (FEM), Linear Isomorphism and Field II. The proposed method considers that the scatterers map is a property of the tissue; therefore, the scatterers should move according to the tissue strain. Methods First, a volume representing the target tissue is loaded. Second, parameter values, such as Young’s Modulus, scatterers density, attenuation and scattering amplitudes are inserted for each different regions of the phantom. Then, other parameters related to the ultrasound equipment, such as ultrasound frequency and number of transducer elements, are also defined in order to perform the ultrasound acquisition using Field II. Third, the size and position of the transducer and the pressures that are applied against the tissue are defined. Subsequently, FEM is executed and deformation is computed. Next, 3D linear isomorphism is performed to displace the scatterers according to the deformation. Finally, Field II is carried out to generate the non-deformed and deformed ultrasound data. Results The framework is evaluated by comparing strain values obtained the numerical simulation and from the physical phantom from CIRS. The mean difference between both phantoms is lesser than 10%. Conclusion The acoustic and deformation outcomes are similar to those obtained using a physical phantom. This framework led to a tool, which is available online and free of charges for educational and research purposes.

Atherosclerotic plaque characterization using plaque area variation in IVUS images during compression: a computational investigation

INTRODUCTION: The rupture of atherosclerotic plaques causes millions of death yearly. It is known that the kind of predominant tissue is associated with its dangerousness. In addition, the mechanical properties of plaques have been proved to be a good parameter to characterize the type of tissue, important information for therapeutic decisions. METHODS: Therefore, we present an alternative and simple way to discriminate tissues. The procedure relies on computing an index, the ratio of the plaque area variation of a suspecting plaque, using images acquired with vessel and plaques, pre and post-deformation, under different intraluminal pressure. Numerical phantoms of coronary cross-sections with different morphological aspects, and simulated with a range of properties, were used for evaluation. RESULTS: The outcomes provided by this index and a widely used one were compared, so as to measure their correspondence. As a result, correlations up to 99%, a strong agreement with Bland-Altman and very similar histograms between the two indices, have shown a good level of equivalence between the methods. CONCLUSION: The results demonstrated that the proposed index discriminates highly lipidic from fibro-lipidic and calcified tissues in many situations, as good as the widely used index, yet the proposed method is much simpler to be computed.

Automatic lumen segmentation in IVOCT images using binary morphological reconstruction.

BACKGROUND: Atherosclerosis causes millions of deaths, annually yielding billions in expenses round the world. Intravascular Optical Coherence Tomography (IVOCT) is a medical imaging modality, which displays high resolution images of coronary cross-section. Nonetheless, quantitative information can only be obtained with segmentation; consequently, more adequate diagnostics, therapies and interventions can be provided. Since it is a relatively new modality, many different segmentation methods, available in the literature for other modalities, could be successfully applied to IVOCT images, improving accuracies and uses. METHOD: An automatic lumen segmentation approach, based on Wavelet Transform and Mathematical Morphology, is presented. The methodology is divided into three main parts. First, the preprocessing stage attenuates and enhances undesirable and important information, respectively. Second, in the feature extraction block, wavelet is associated with an adapted version of Otsu threshold; hence, tissue information is discriminated and binarized. Finally, binary morphological reconstruction improves the binary information and constructs the binary lumen object. RESULTS: The evaluation was carried out by segmenting 290 challenging images from human and pig coronaries, and rabbit iliac arteries; the outcomes were compared with the gold standards made by experts. The resultant accuracy was obtained: True Positive (%) = 99.29 ± 2.96, False Positive (%) = 3.69 ± 2.88, False Negative (%) = 0.71 ± 2.96, Max False Positive Distance (mm) = 0.1 ± 0.07, Max False Negative Distance (mm) = 0.06 ± 0.1. CONCLUSIONS: In conclusion, by segmenting a number of IVOCT images with various features, the proposed technique showed to be robust and more accurate than published studies; in addition, the method is completely automatic, providing a new tool for IVOCT segmentation.

Segmentação do lúmen em imagens de IOCT usando Fuzzy Connectedness e Reconstrução Binária Morfológica / Lumen Segmentation in IOCT images using Fuzzy and Binary Morphological Reconstruction

No ano 2010, doenças cardiovasculares (CVD) causaram 33% do total das mortes no Brasil. Tomografia Ótica Coerente Intravascular (IOCT) é uma tecnologia que oferece imagens in vivo para detecção e monitoramento da progressão de CVD. O exame de IOCT permite mais precisão no diagnóstico; contudo, ainda é pequena a variedade de métodos quantitativos aplicados a IOCT na literatura, em comparação à outras modalidades relacionadas. Portanto neste trabalho é proposto um método de segmentação do lúmen, baseado em uma combinação de Fuzzy Connectedness, com múltiplas funções de afinidade, e Operações Morfológicas. As funções de afinidade usadas neste trabalho são: (I) Clássica, (II) Pesos Dinâmicos e (III) Bhattacharyya. Esta última é baseada no coeficiente de Bhattacharyya, utilizado habitualmente para speckle tracking. Primeiro, características não desejadas da imagem são atenuadas. Depois, informações da parede do vaso são obtidas utilizando Fuzzy Connectedness e um processo de binarização dinâmico. Finalmente, operações morfológicas são realizadas para melhorar o lúmen segmentado. Para avaliar o método proposto, um conjunto de 130 imagens advindas de humanos, porcos, e coelhos foram segmentadas e comparadas com seus respectivos "Gold Standards" feitos por especialistas. Uma média de verdadeiros positivos (TP%) = 98,08 e de falsos positivos (FP%) = 2,34 foram obtidas. Com isso, o método proposto resultou em uma maior eficácia do que os estudos publicados anteriormente, encorajando seu uso.

In 2010 cardiovascular disease (CVD) caused 33% of the total deaths in Brazil. Intravascular Optical Coherent Tomography (IOCT) is an imaging technology that provides in vivo detection and monitoring of the progression of coronary heart disease. IOCT exam allows more accurate diagnoses; nonetheless, the set of quantitative methods applied to IOCT in the literature is small compared to other related modalities. Therefore, the proposed approach presents a lumen segmentation method, based on a combination of Fuzzy Connectedness, with multiple affinity functions, and Morphological Operations. The affinity functions used in this work are: (I) classical, (II) Dynamic weights (III) Bhattacharyya. The latter is based on the Bhattacharyya coefficient, commonly used for speckle tracking. Firstly, unwanted features of the image are attenuated. Then, vessel-wall information is obtained using Fuzzy Connectedness and dynamic binarization process. Finally, morphological operations are performed to improve the segmented lumen. To evaluate the proposed method, a set of 130 images from humans, pigs and rabbits were segmented and compared to their corresponding gold standard made by experts. An average of true positive (TP%) = 98.08, and false positive (FP%) = 2.34 were obtained. Hence, the use of the proposed method is suggested since it has yielded higher efficiency than previously published studies.

Realistic IVUS image generation in different intraluminal pressures.

Intravascular ultrasound (IVUS) phantoms are important to calibrate and evaluate many IVUS imaging processing tasks. However, phantom generation is never the primary focus of related works; hence, it cannot be well covered, and is usually based on more than one platform, which may not be accessible to investigators. Therefore, we present a framework for creating representative IVUS phantoms, for different intraluminal pressures, based on the finite element method and Field II. First, a coronary cross-section model is selected. Second, the coronary regions are identified to apply the properties. Third, the corresponding mesh is generated. Fourth, the intraluminal force is applied and the deformation computed. Finally, the speckle noise is incorporated. The framework was tested taking into account IVUS contrast, noise and strains. The outcomes are in line with related studies and expected values. Moreover, the framework toolbox is freely accessible and fully implemented in a single platform.

Automatic coronary wall segmentation in intravascular ultrasound images using binary morphological reconstruction.

Intravascular ultrasound (IVUS) image segmentation can provide more detailed vessel and plaque information, resulting in better diagnostics, evaluation and therapy planning. A novel automatic segmentation proposal is described herein; the method relies on a binary morphological object reconstruction to segment the coronary wall in IVUS images. First, a preprocessing followed by a feature extraction block are performed, allowing for the desired information to be extracted. Afterward, binary versions of the desired objects are reconstructed, and their contours are extracted to segment the image. The effectiveness is demonstrated by segmenting 1300 images, in which the outcomes had a strong correlation to their corresponding gold standard. Moreover, the results were also corroborated statistically by having as high as 92.72% and 91.9% of true positive area fraction for the lumen and media adventitia border, respectively. In addition, this approach can be adapted easily and applied to other related modalities, such as intravascular optical coherence tomography and intravascular magnetic resonance imaging.

An approach to automatically segment the media-adventitia borders in IVUS / Uma associação de técnicas para segmentação automática da fronteira da média-adventícia em IVUS

Por ser capaz de mostrar aspectos morfológicos e patológicos de ateroscleroses, o Ultrassom Intravascular (IVUS) se tornou uma das modalidades de imagens médicas mais confiáveis e empregadas em intervenções cardíacas. As características de sua imagem aumentam as chances de um bom diagnóstico, resultando em terapias mais precisas. O estudo de segmentação da fronteira média-adventícia, dentre muitas aplicações, é importante para o aprendizado das propriedades mecânicas e determinação de algumas medidas específicas (raio, diâmetro, etc.) em vasos e placas. Neste trabalho, uma associação de técnicas de processamento de imagens está sendo proposta para atingir alta acurácia na segmentação da borda média-adventícia. Para tanto, foi feita uma combinação das seguintes técnicas: Redução do Speckle por Difusão Anisotrópica (SRAD), Wavelet, Otsu e Morfologia Matemática. Primeiramente, é usado SRAD para atenuar os ruídos speckle. Posteriormente, é executada Transformada Wavelet para extração das características dos vasos e placas. Uma versão binarizada dessas características é criada na qual o limiar ótimo é definido por Otsu. Finalmente, é usada Morfologia Matemática para obtenção do formato da adventícia. O método proposto é avaliado ao segmentar 100 imagens de alta complexidade, obtendo uma média de Verdadeiro Positivo (TP(%)) = 92,83 ± 4,91, Falso Positivo (FP(%)) = 3,43 ± 3,47, Falso Negativo (FN(%)) = 7,17 ± 4,91, Máximo Falso Positivo (MaxFP(mm)) = 0,27 ± 0,22, Máximo Falso Negativo (MaxFN(mm)) = 0,31 ± 0,2. A eficácia do nosso método é demonstrada, comparando este resultado com outro trabalho recente na literatura.

By being able to show morphological and pathological aspects of atherosclerosis, the Intravascular Ultrasound (IVUS) be¬came one of the most reliable and employed medical imaging modality in cardiac interventions. Its image characteristics in¬crease the chances of a good diagnostic, resulting in a precise therapy. The study of media-adventitia borders segmentation in IVUS, among many applications, is important for learning about the mechanical properties and determining some specific measurements (radius, diameter, etc.) in vases and plaques. An approach is proposed to achieve high accuracy in media-adventitia borders segmentation, by making a combination of different image processing operations: Speckle Reducing Anisotropic Diffusion (SRAD), Wavelet, Otsu and Mathematical Morphology. Firstly, SRAD is applied to attenuate the speckle noise. Next, the vessel and plaque features are extracted by performing Wavelet Transform. Optimal thresholding is car¬ried out by Otsu method to create a binarized version of these features. Then, Mathematical Morphology operations are used to obtain an adventitia shape. The proposed approach is evaluated by segmenting 100 challenging images, obtaining an average of True Positive (TP(%)) = 92.83 ± 4.91, False Positive (FP(%)) = 3.43 ± 3.47, False Negative (FN(%)) = 7.17 ± 4.91, Max False Positive (MaxFP(mm)) = 0.27 ± 0.22, Max False Negative (MaxFN(mm)) = 0.31 ± 0.2. The effectiveness of our approach is demonstrated by comparing this result with another recent work in the literature.