A machine learning approach to automatic detection of irregularity in skin lesion border using dermoscopic images

Skin lesion border irregularity is considered an important clinical feature for the early diagnosis of melanoma, representing the B feature in the ABCD rule. In this article we propose an automated approach for skin lesion border irregularity detection. The approach involves extracting the skin lesion from the image, detecting the skin lesion border, measuring the border irregularity, training a Convolutional Neural Network and Gaussian naive Bayes ensemble, to the automatic detection of border irregularity, which results in an objective decision on whether the skin lesion border is considered regular or irregular. The approach achieves outstanding results, obtaining an accuracy, sensitivity, specificity, and F-score of 93.6%, 100%, 92.5% and 96.1%, respectively

A survey on utilization of data mining approaches for dermatological (skin) diseases prediction

Due to recent technology advances, large volumes of medical data is obtained. These data contain valuable information. Therefore data mining techniques can be used to extract useful patterns. This paper is intended to introduce data mining and its various techniques and a survey of the available literature on medical data mining. We emphasize mainly on the application of data mining on skin diseases. A categorization has been provided based on the different data mining techniques. The utility of the various data mining methodologies is highlighted. Generally association mining is suitable for extracting rules. It has been used especially in cancer diagnosis. Classification is a robust method in medical mining. In this paper, we have summarized the different uses of classification in dermatology. It is one of the most important methods for diagnosis of erythemato-squamous diseases. There are different methods like Neural Networks, Genetic Algorithms and fuzzy classifiaction in this topic. Clustering is a useful method in medical images mining. The purpose of clustering techniques is to find a structure for the given data by finding similarities between data according to data characteristics. Clustering has some applications in dermatology. Besides introducing different mining methods, we have investigated some challenges which exist in mining skin data

Computer aided diagnosis system using dermatoscopical image

Computer Aided Diagnosis (CAD) systems for melanoma detection aim to mirror the expert dermatologist decision when watching a dermoscopic or clinical image. Computer Vision techniques, which can be based on expert knowledge or not, are used to characterize the lesion image. This information is delivered to a machine learning algorithm, which gives a diagnosis suggestion as an output. This research is included into this field, and addresses the objective of implementing a complete CAD system using ‘state of the art’ descriptors and dermoscopy images as input. Some of them are based on expert knowledge and others are typical in a wide variety of problems. Images are initially transformed into oRGB, a perceptual color space, looking for both enhancing the information that images provide and giving human perception to machine algorithms. Feature selection is also performed to find features that really contribute to discriminate between benign and malignant pigmented skin lesions (PSL). The problem of robust model fitting versus statistically significant system evaluation is critical when working with small datasets, which is indeed the case. This topic is not generally considered in works related to PSLs. Consequently, a method that optimizes the compromise between these two goals is proposed, giving non-overfitted models and statistically significant measures of performance. In this manner, different systems can be compared in a fairer way. A database which enjoys wide international acceptance among dermatologists is used for the experiments.Ingeniería de Sistemas Audiovisuale

Automating the ABCD Rule for Melanoma Detection: A Survey

The ABCD rule is a simple framework that physicians, novice dermatologists and non-physicians can use to learn about the features of melanoma in its early curable stage, enhancing thereby the early detection of melanoma. Since the interpretation of the ABCD rule traits is subjective, different solutions have been proposed in literature to tackle such subjectivity and provide objective evaluations to the different traits. This paper reviews the main contributions in literature towards automating asymmetry, border irregularity, color variegation and diameter, where the different methods involved have been highlighted. This survey could serve as an essential reference for researchers interested in automating the ABCD rule

Evaluation of different segmentation-based approaches for skin disorders from dermoscopic images

Treballs Finals de Grau d'Enginyeria Biomèdica. Facultat de Medicina i Ciències de la Salut. Universitat de Barcelona. Curs: 2022-2023. Tutor/Director: Sala Llonch, Roser, Mata Miquel, Christian, Munuera, JosepSkin disorders are the most common type of cancer in the world and the incident has been lately increasing over the past decades. Even with the most complex and advanced technologies, current image acquisition systems do not permit a reliable identification of the skin lesion by visual examination due to the challenging structure of the malignancy. This promotes the need for the implementation of automatic skin lesion segmentation methods in order to assist in physicians’ diagnostic when determining the lesion's region and to serve as a preliminary step for the classification of the skin lesion. Accurate and precise segmentation is crucial for a rigorous screening and monitoring of the disease's progression. For the purpose of the commented concern, the present project aims to accomplish a state-of-the-art review about the most predominant conventional segmentation models for skin lesion segmentation, alongside with a market analysis examination. With the rise of automatic segmentation tools, a wide number of algorithms are currently being used, but many are the drawbacks when employing them for dermatological disorders due to the high-level presence of artefacts in the image acquired. In light of the above, three segmentation techniques have been selected for the completion of the work: level set method, an algorithm combining GrabCut and k-means methods and an intensity automatic algorithm developed by Hospital Sant Joan de Déu de Barcelona research group. In addition, a validation of their performance is conducted for a further implementation of them in clinical training. The proposals, together with the got outcomes, have been accomplished by means of a publicly available skin lesion image database

A review of the role of ultrasound biomicroscopy in glaucoma associated with rare diseases of the anterior segment

Ultrasound biomicroscopy is a non-invasive imaging technique, which allows high-resolution evaluation of the anatomical features of the anterior segment of the eye regardless of optical media transparency. This technique provides diagnostically significant information in vivo for the cornea, anterior chamber, chamber angle, iris, posterior chamber, zonules, ciliary body, and lens, and is of great value in assessment of the mechanisms of glaucoma onset. The purpose of this paper is to review the use of ultrasound biomicroscopy in the diagnosis and management of rare diseases of the anterior segment such as mesodermal dysgenesis of the neural crest, iridocorneal endothelial syndrome, phakomatoses, and metabolic disorders

Histopathological correlates of the biological variation in primary melanomas

Primary cutaneous melanoma is a highly heterogeneous tumour. My hypothesis is that the histopathological heterogeneity reflects biological variation, which is likely to have prognostic and predictive significance. A histopathological review of 798 primary melanomas from the Leeds Melanoma Cohort Study was recorded using virtual pathology. The tumour blocks had previously been sampled using a tissue microarray needle, yielding a core from which RNA was extracted and assayed using Illumina® WG-DASL. This provided the opportunity to compare histopathological characteristics with gene expression data derived from consistently sampled regions. RandomSpot© was used to estimate the percentage of stroma (POS) within cored regions. Statistical analyses were performed using STATA v14.2. Inter- and intraobserver agreement were analysed and robust measures were retained. Histopathological characteristics were analysed with respect to germline BAP1 mutation status to assess whether they could predict germline BAP1 mutation status. BAP-like histopathology was not significantly associated with germline BAP1 mutation status (deleterious versus none, Fisher’s exact test, p=0.1). A personal history of mesothelioma (Fisher’s exact test, p=0.005), or a family history of meningioma (Fisher’s exact test, p=0.005) or BCC (Fisher’s exact test, p=0.02) was associated with deleterious, germline BAP1 mutations. Cancer history appeared to be a better indicator of germline BAP1 mutation status than BAP-like histopathology. Several histopathological factors were predictive of melanoma-specific survival, including the POS. The area under the curve increased by 3% when the POS and AJCC stage were combined in ROC curve analysis. The POS was an independent predictor of melanoma-specific survival (HR 0.99, 95% CI 0.98-0.99, Cox proportional hazards model, p=0.005), even adjusting for known prognostic factors. SDF1 gene expression was significantly associated with the POS and was independently protective for melanoma-specific death (HR 0.8, 95% CI 0.68-0.94, Cox proportional hazards model, p=0.005) in adjusted analyses. The POS and SDF1 could represent novel predictive and prognostic biomarkers

Histopathological correlates of the biological variation in primary melanomas

Primary cutaneous melanoma is a highly heterogeneous tumour. My hypothesis is that the histopathological heterogeneity reflects biological variation, which is likely to have prognostic and predictive significance. A histopathological review of 798 primary melanomas from the Leeds Melanoma Cohort Study was recorded using virtual pathology. The tumour blocks had previously been sampled using a tissue microarray needle, yielding a core from which RNA was extracted and assayed using Illumina® WG-DASL. This provided the opportunity to compare histopathological characteristics with gene expression data derived from consistently sampled regions. RandomSpot© was used to estimate the percentage of stroma (POS) within cored regions. Statistical analyses were performed using STATA v14.2. Inter- and intraobserver agreement were analysed and robust measures were retained. Histopathological characteristics were analysed with respect to germline BAP1 mutation status to assess whether they could predict germline BAP1 mutation status. BAP-like histopathology was not significantly associated with germline BAP1 mutation status (deleterious versus none, Fisher’s exact test, p=0.1). A personal history of mesothelioma (Fisher’s exact test, p=0.005), or a family history of meningioma (Fisher’s exact test, p=0.005) or BCC (Fisher’s exact test, p=0.02) was associated with deleterious, germline BAP1 mutations. Cancer history appeared to be a better indicator of germline BAP1 mutation status than BAP-like histopathology. Several histopathological factors were predictive of melanoma-specific survival, including the POS. The area under the curve increased by 3% when the POS and AJCC stage were combined in ROC curve analysis. The POS was an independent predictor of melanoma-specific survival (HR 0.99, 95% CI 0.98-0.99, Cox proportional hazards model, p=0.005), even adjusting for known prognostic factors. SDF1 gene expression was significantly associated with the POS and was independently protective for melanoma-specific death (HR 0.8, 95% CI 0.68-0.94, Cox proportional hazards model, p=0.005) in adjusted analyses. The POS and SDF1 could represent novel predictive and prognostic biomarkers