Recognition Of Melanoma Research Articles

Hunting for Early Melanomas With a Maximum Clinical Surface Diameter up to 6 mm: A Prospective Study of Reflectance Confocal Microscopy Features in 68 Consecutive Cases.

Early diagnosis of melanoma and prompt effective therapy optimizes prognosis. Reflectance confocal microscopy (RCM) facilitates diagnosis by providing immediate 3D single cell resolution down into the papillary dermis. Consecutive cases were examined using a Vivascope 1500 confocal microscope at a single referral medical practice in Sydney, Australia 2019-2023. Melanoma clinical surface diameters were recorded by 0.1 mm increments up to 6.0 mm. The RCM features recorded were: pagetoid single cells or nests, pleomorphic cell shape, atypical dendritic cells, non-edged papillae, variation in melanocyte size and confluent sheets of cells. All cases required diagnostic agreement by two dermatohistopathologists using hematoxylin and eosin staining followed by SOX 10 and/or PRAME stains if required. Total cases were 68: 38 males (mean age 57) and 30 females (mean age 64). Melanoma in situ (n = 65) compared to invasive melanoma (n = 3), all males, invasion depth (0.4-0.5 mm). Most frequent RCM features found in 50% or more of cases within all diameter increments were: pagetoid single cells n = 64/68 (94%), pleomorphic cell shape n = 63/68 (93%), epidermal disarray n = 58/68 (85%), and atypical dendritic cells n = 45/68 (66%). Non-edged dermal papillae were n = 42/68 (62%). Melanoma RCM features were found throughout the diameter ranges. Confocal examination may facilitate early melanoma recognition in these ranges.

Artificial Intelligence in Dermatopathology: a systematic review.

Medical research, driven by advancing technologies like Artificial Intelligence (AI), is transforming healthcare. Dermatology, known for its visual nature, benefits from AI, especially in dermatopathology with digitized slides. This review explores into AI's role, challenges, opportunities, and future potential in enhancing dermatopathological diagnosis and care. Adhering to PRISMA and Cochrane Handbook standards, this systematic review explored AI's function in dermatopathology. It employed an interdisciplinary method, encompassing diverse study types and comprehensive database searches. Inclusion criteria encompassed peer-reviewed articles from 2000 to 2023, with a focus on practical AI use in dermatopathology. Numerous studies have investigated AI's potential in dermatopathology. We reviewed 112 papers. Notable applications include AI classifying histopathological images of nevi and melanomas, although challenges exist regarding subtype differentiation and generalizability. AI achieved high accuracy in melanoma recognition from formalin-fixed paraffin-embedded samples but faced limitations due to small datasets. Deep learning algorithms showed diagnostic accuracy for specific skin conditions, but challenges persisted, such as small sample sizes and the need for prospective validation. This systematic review underscores AI's potential in enhancing dermatopathology for better diagnosis and patient care. Addressing challenges like limited datasets and potential biases is essential. Future directions involve expanding datasets, conducting validation studies, promoting interdisciplinary collaboration, and creating patient-centred AI tools to enhance dermatopathology's accuracy, accessibility, and patient-focused care.

Spitz melanoma

It has long been recognized that the histopathologic differential diagnosis between Spitz nevus and melanoma is sometimes extraordinarily difficult. Both can be composed of large oval to spindled melanocytes with abundant cytoplasm and large nuclei. Genomic studies over the last decade have clarified that Spitz tumors have diverse genetic initiating events, and that for each of these, there are benign, intermediate grade, and malignant lesions. Another discovery is that some melanomas can morphologically resemble Spitz tumors but have conventional initiating mutations (e.g., BRAF, NRAS). The current WHO definition of Spitz tumors restricts the spectrum to neoplasms with an activating mutation affecting the MAP kinase pathway, mostly fusions involving kinase genes. Whether splitting off Spitz tumors from other neoplasms that are only morphologically spitzoid will stand the test of time is uncertain, but for now, it does bring order to what has been a complex area. The recognition of Spitz melanoma will enable classification, follow-up, and the delineation of rational treatment guidelines for this important group of tumors, most of which are in young people. I have reviewed the logic of the WHO definition of Spitz tumors, the spectrum of tumors produced by initiating mutations, problems with the definition of Spitz melanoma, and have provided an example of this rare entity in the context of related tumors.

Incidental Cutaneous Melanoma

A young man admitted to the hospital with sepsis and cellulitis was incidentally found to have invasive cutaneous melanoma. Recognition of melanoma is important to ensure timely diagnosis and treatment.

Patient and dermatologists' perspectives on augmented intelligence for melanoma screening: A prospective study.

Artificial intelligence (AI) shows promising potential to enhance human decision-making as synergistic augmented intelligence (AuI), but requires critical evaluation for skin cancer screening in a real-world setting. To investigate the perspectives of patients and dermatologists after skin cancer screening by human, artificial and augmented intelligence. A prospective comparative cohort study conducted at the University Hospital Basel included 205 patients (at high-risk of developing melanoma, with resected or advanced disease) and 8 dermatologists. Patients underwent skin cancer screening by a dermatologist with subsequent 2D and 3D total-body photography (TBP). Any suspicious and all melanocytic skin lesions ≥3 mm were imaged with digital dermoscopes and classified by corresponding convolutional neural networks (CNNs). Excisions were performed based on dermatologist's melanoma suspicion, study-defined elevated CNN risk-scores and/or melanoma suspicion by AuI. Subsequently, all patients and dermatologists were surveyed about their experience using questionnaires, including quantification of patient's safety sense following different examinations (subjective safety score (SSS): 0-10). Most patients believed AI could improve diagnostic performance (95.5%, n = 192/201). In total, 83.4% preferred AuI-based skin cancer screening compared to examination by AI or dermatologist alone (3D-TBP: 61.3%; 2D-TBP: 22.1%, n = 199). Regarding SSS, AuI induced a significantly higher feeling of safety than AI (mean-SSS (mSSS): 9.5 vs. 7.7, p < 0.0001) or dermatologist screening alone (mSSS: 9.5 vs. 9.1, p = 0.001). Most dermatologists expressed high trust in AI examination results (3D-TBP: 90.2%; 2D-TBP: 96.1%, n = 205). In 68.3% of the examinations, dermatologists felt that diagnostic accuracy improved through additional AI-assessment (n = 140/205). Especially beginners (<2 years' dermoscopic experience; 61.8%, n = 94/152) felt AI facilitated their clinical work compared to experts (>5 years' dermoscopic experience; 20.9%, n = 9/43). Contrarily, in divergent risk assessments, only 1.5% of dermatologists trusted a benign CNN-classification more than personal malignancy suspicion (n = 3/205). While patients already prefer AuI with 3D-TBP for melanoma recognition, dermatologists continue to rely largely on their own decision-making despite high confidence in AI-results. ClinicalTrials.gov (NCT04605822).

Preliminary analysis of the melanoma multimedia educational program for general practitioners on behalf of the Italian Melanoma Intergroup.

According to the National Oncological Plan 2023-2027 on the importance of multidisciplinary and interactive e-learning training, the Italian Melanoma Intergroup (IMI) has developed MelaMEd (Melanoma Multimedia Education), a national project for general practitioners (GPs) on the prevention and detection of cutaneous melanoma through an online platform and an online course. MelaMEd enables participants to i) recognize skin lesions that require specialist dermatological assessment, ii) select patients at high risk of melanoma and iii) be informed of the diagnosis and treatment pathway of patients with melanoma. A free online platform and online course were developed and launched in June 2022. Before starting the course, enrolled participants fill out a pre-training questionnaire concerning the basic knowledge of the disease and the recognition and management of suspicious lesions. After the course, participants will fill out the same questionnaire again. The online course will end in December 2023. Here we present a preliminary analysis of the pre-training results (January 2023-July 2023). The data have been analyzed descriptively. So far, five healthcare centers have participated in the project for a total of 1320 participants. Of these, 298 compiled the pre-training questionnaire. Forty-seven percent of them were aged <40 years. Respondents were almost divided between GPs (47%) and resident GPs (48%). Among the theoretical questions, the ABCDE rule and ugly duckling sign are well known (96% and 91% of correct answers, respectively), but a lower percentage (68%) of respondents knows the EFG rule for the recognition of nodular melanomas and the statement of Breslow thickness (29%). Regarding the series of clinical images of pigmented skin lesions and their management, the percentages rate of accuracy varied from 33% to 87%: melanoma (5 cases) ranges from 36% to 71%, melanocytic nevi (3 cases) from 33% to 84%, whereas the percentages rate of referral for dermatological evaluation varied from 44% to 99%. Melanoma cases referred to dermatologist ranges from 67% to 99%. This preliminary analysis on pre-train-ing questionnaire mainly showed a lack of knowledge of the two major points of melanoma diagnosis (EFG) and management (Breslow thickness), as well as a low rate of participants. We will compare the proportions of correct answers to the questionnaires before and after the course once available.

Consistency of convolutional neural networks in dermoscopic melanoma recognition: A prospective real-world study about the pitfalls of augmented intelligence.

Deep-learning convolutional neural networks (CNNs) have outperformed even experienced dermatologists in dermoscopic melanoma detection under controlled conditions. It remains unexplored how real-world dermoscopic image transformations affect CNN robustness. To investigate the consistency of melanoma risk assessment by two commercially available CNNs to help formulate recommendations for current clinical use. A comparative cohort study was conducted from January to July 2022 at the Department of Dermatology, University Hospital Basel. Five dermoscopic images of 116 different lesions on the torso of 66 patients were captured consecutively by the same operator without deliberate rotation. Classification was performed by two CNNs (CNN-1/CNN-2). Lesions were divided into four subgroups based on their initial risk scoring and clinical dignity assessment. Reliability was assessed by variation and intraclass correlation coefficients. Excisions were performed for melanoma suspicion or two consecutively elevated CNN risk scores, and benign lesions were confirmed by expert consensus (n = 3). 117 repeated image series of 116 melanocytic lesions (2 melanomas, 16 dysplastic naevi, 29 naevi, 1 solar lentigo, 1 suspicious and 67 benign) were classified. CNN-1 demonstrated superior measurement repeatability for clinically benign lesions with an initial malignant risk score (mean variation coefficient (mvc): CNN-1: 49.5(±34.3)%; CNN-2: 71.4(±22.5)%; p = 0.03), while CNN-2 outperformed for clinically benign lesions with benign scoring (mvc: CNN-1: 49.7(±22.7)%; CNN-2: 23.8(±29.3)%; p = 0.002). Both systems exhibited lowest score consistency for lesions with an initial malignant risk score and benign assessment. In this context, averaging three initial risk scores achieved highest sensitivity of dignity assessment (CNN-1: 94%; CNN-2: 89%). Intraclass correlation coefficients indicated 'moderate'-to-'good' reliability for both systems (CNN-1: 0.80, 95% CI:0.71-0.87, p < 0.001; CNN-2: 0.67, 95% CI:0.55-0.77, p < 0.001). Potential user-induced image changes can significantly influence CNN classification. For clinical application, we recommend using the average of three initial risk scores. Furthermore, we advocate for CNN robustness optimization by cross-validation with repeated image sets. ClinicalTrials.gov (NCT04605822).

A Case Series of Rare Vaginal Malignant Melanoma

Introduction: Vaginal melanoma is a rare malignancy. Sadly, melanoma is a very aggressive tumor with a 5-year survival rate of 5%–25%. There is no publication about the incidence of vaginal malignant melanoma in Indonesia. Due to the lack of cases in Indonesia, this study aims to report the challenges and explore the risk factor, treatment, and prognosis of Vaginal Malignant Melanoma. Case Presentation: This case series report 2 patient with vaginal malignant melanoma who were diagnosed at Dharmais Cancer Center Hospital. Conclusions: Diagnosis and optimal treatment plans can be established through rapid and precise recognition of Malignant Melanoma to achieve better outcomes.

PA02 A teenager with a bleeding neck nodule

Abstract A 14-year-old boy was referred to dermatology with a new bleeding lesion on his right neck that had grown rapidly. There was no preceding history of trauma. Physical examination revealed a discrete eroded pink nodule measuring 10 × 9 mm on his right neck. Dermoscopic examination showed a central area of ulceration, with polymorphous vessels, white lines and multiple shades of pink and milky-white areas. This was excised. Histology confirmed the diagnosis of ulcerated superficial spreading malignant melanoma with a Breslow thickness of 4.8 mm (pT4b). BRAF v600e mutation was present. Initial staging with sentinel lymph node biopsy and whole-body positron emission tomography/­computed tomography were clear, but 6 months after presentation, he developed right-sided cervical lymphadenopathy and a parotid mass that was confirmed to be metastatic disease. Following a right parotidectomy and neck dissection, he was commenced on adjuvant treatment with dabrafenib and trametinib. He remains under close follow-up. Less than 1% of all melanomas are diagnosed in patients younger than 20 years of age. Most paediatric cases occur in adolescence (between 15 and 19 years of age). The majority arise sporadically. Risk factors for melanoma in childhood include giant congenital melanocytic naevi, personal history of malignancy, family history of melanoma, multiple (&amp;gt; 50) melanocytic naevi, history of sunburn or tanning-bed use, immunosuppression or genodermatoses (e.g. xeroderma pigmentosum). Most clinicians are familiar with the ‘ABCDE’ tool (asymmetry, border irregularity, colour variegation, diameter &amp;gt; 6 mm, evolution) used to help with the recognition of melanomas; however, melanoma in children often presents in an atypical fashion. Paediatric melanomas often lack pigment (amelanotic) and are nodular, and so may be misdiagnosed. Because of this, diagnosis may be delayed and patients often present with thicker tumours and lymph node involvement. To facilitate earlier diagnosis, modified ABCD detection criteria have been proposed for paediatric melanoma (amelanosis, bleeding/bump, colour uniformity, de novo development) that can be used in conjunction with the established ABCDE approach. The CUP criteria have also been described (colour that is pink/red/changing, ulceration/upward thickness, pyogenic granuloma-like lesions/pop-up of new lesions). Evolution remains an important point to consider when suspecting melanoma in patients of any age. Management and staging of paediatric melanoma is adopted from the American Joint Committee on Cancer melanoma staging system guidelines for adult melanoma. Currently, there are no specific guidelines for the management of melanoma in children. The mainstay of treatment is surgical resection. Sentinel lymph node biopsy should be considered with ulceration or a Breslow thickness of &amp;gt; 0.75 mm. Patients with stage III and IV melanoma should be considered for adjuvant immunotherapy. All patients with melanoma should be followed-up with regular skin examinations.

STNet: shape and texture joint learning through two-stream network for knowledge-guided image recognition.

The human brain processes shape and texture information separately through different neurons in the visual system. In intelligent computer-aided imaging diagnosis, pre-trained feature extractors are commonly used in various medical image recognition methods, common pre-training datasets such as ImageNet tend to improve the texture representation of the model but make it ignore many shape features. Weak shape feature representation is disadvantageous for some tasks that focus on shape features in medical image analysis. Inspired by the function of neurons in the human brain, in this paper, we proposed a shape-and-texture-biased two-stream network to enhance the shape feature representation in knowledge-guided medical image analysis. First, the two-stream network shape-biased stream and a texture-biased stream are constructed through classification and segmentation multi-task joint learning. Second, we propose pyramid-grouped convolution to enhance the texture feature representation and introduce deformable convolution to enhance the shape feature extraction. Third, we used a channel-attention-based feature selection module in shape and texture feature fusion to focus on the key features and eliminate information redundancy caused by feature fusion. Finally, aiming at the problem of model optimization difficulty caused by the imbalance in the number of benign and malignant samples in medical images, an asymmetric loss function was introduced to improve the robustness of the model. We applied our method to the melanoma recognition task on ISIC-2019 and XJTU-MM datasets, which focus on both the texture and shape of the lesions. The experimental results on dermoscopic image recognition and pathological image recognition datasets show the proposed method outperforms the compared algorithms and prove the effectiveness of our method.

Residual neural network‐assisted one‐class classification algorithm for melanoma recognition with imbalanced data

AbstractSkin cancer, also known as melanoma, is a deadly form of skin cancer that can significantly improve survival rates when diagnosed at an early stage. It is usually diagnosed visually from dermoscopic images, and such visual assessment of skin cancer by the naked eye is a challenging and arduous task. Therefore, the detection of melanoma from dermoscopic images using trained artificial intelligence models is of great importance today. However, since melanoma is a rare disease, existing databases of skin lesions often contain highly unbalanced numbers of benign and malignant samples. In this paper, we propose a new one‐class classification‐based skin lesion classification strategy for small and unbalanced datasets. One‐class classification (OCC) is a special case of multi‐classification. OCC aims to learn a descriptive paradigm from positive class data (true data) during training and reject pseudo data (fake data) that do not conform to the paradigm during inference. OCC has great potential for application in anomaly detection problems. We have analyzed several approaches to the OCC task in recent years and propose a new design paradigm for the OCC problem, taking into account the unbalanced data set of the melanoma classification task. We have designed an improved OCC network based on this design paradigm, where the network is based on the architecture of a residual neural network, combining the coding and decoding idea of variational self‐encoder and the adversarial training idea of an adversarial neural network, using binary cross‐entropy as the loss function and introducing the channel attention mechanism. Tests on several publicly available dermatology datasets show that this improved OCC network addresses the unbalanced dataset situation in melanoma image classification to some extent while having relatively excellent performance. Compared with some traditional networks, it can obtain more stable training results and perform more consistently on complex datasets.

Diagnosing Melanomas in Dermoscopy Images Using Deep Learning.

When it comes to skin tumors and cancers, melanoma ranks among the most prevalent and deadly. With the advancement of deep learning and computer vision, it is now possible to quickly and accurately determine whether or not a patient has malignancy. This is significant since a prompt identification greatly decreases the likelihood of a fatal outcome. Artificial intelligence has the potential to improve healthcare in many ways, including melanoma diagnosis. In a nutshell, this research employed an Inception-V3 and InceptionResnet-V2 strategy for melanoma recognition. The feature extraction layers that were previously frozen were fine-tuned after the newly added top layers were trained. This study used data from the HAM10000 dataset, which included an unrepresentative sample of seven different forms of skin cancer. To fix the discrepancy, we utilized data augmentation. The proposed models outperformed the results of the previous investigation with an effectiveness of 0.89 for Inception-V3 and 0.91 for InceptionResnet-V2.

Az elmúlt húsz év kihívásai és előrelépései dermatoonkológiai centrumunkban

In the recent work, the authors provide an overview of the significant advances and challenges during the last two decades of the activities of the oncodermatology working group. In the field of the primary and secondary prevention, the focus of interest was the early recognition of melanoma, a more precise understanding of the rare types of the disease, and an improvement of the population's skin cancer-related knowledge and sun protection habits. In the field of tertiary prevention, the emergence of targeted and immunotherapy represented a major breakthrough. The new opportunities and the change in attitude have resulted in the fact, that nowadays we can treat our skin cancer patients with improved survival rate and better quality of life, comparable to other European centers.

Impact of the COVID-19 pandemic on melanoma diagnosis and presentation: a `review.

The ongoing challenges posed by COVID-19 are concerning for their impact on successful detection and recognition of melanoma as total body skin examinations and skin biopsies are critical for identifying early-stage melanoma and intervening before progression to metastatic disease. A comprehensive electronic search of PubMed/MEDLINE was conducted on or before August 1, 2022, using the search terms ("skin" AND "COVID-19"), (["skin cancer" AND "COVID-19"] OR ["skin cancer" AND "coronavirus"]), (["melanoma" AND "COVID-19"] OR ["melanoma" AND "coronavirus"]), ("dermatology" AND "COVID-19"), and ("cutaneous" AND "COVID-19"). Eight articles representing Belgium, Chile, France, Germany, Spain, the United Kingdom, and the United States were included. Four articles analyzed changes in the proportion of in situ melanoma at diagnosis and consistently reported decreases, with an overall decrease ranging from 7.6 to 40.4%. Five studies analyzed changes in the proportion of melanoma diagnoses by staging, but no clear changes in staging patterns were observed. Five studies analyzed changes in the mean Breslow thickness of melanoma diagnoses and consistently reported increases, with an overall increase ranging from 4.0 to 38%. Disruptions to proper diagnosis and treatment of melanoma are creating undue morbidity, mortality, and healthcare costs as the pandemic continues. Continued research with improved, centralized data collection is needed to better address the COVID-19 pandemic's ongoing challenge to appropriate detection and treatment of melanoma.

The NKcell receptor NKp46 recognizes ecto-calreticulin on ER-stressed cells.

Natural killer (NK) cell kill infected, transformed and stressed cells when an activating NK cellreceptor is triggered1. Most NKcells and some innate lymphoid cells express the activating receptor NKp46, encoded by NCR1, the most evolutionarily ancient NKcell receptor2,3. Blockage of NKp46 inhibits NK killing of many cancer targets4. Although a few infectious NKp46 ligands have been identified, the endogenous NKp46 cell surface ligand is unknown. Here we show that NKp46 recognizes externalized calreticulin (ecto-CRT), which translocates from the endoplasmic reticulum (ER) to the cell membrane during ER stress. ER stress and ecto-CRT are hallmarks of chemotherapy-induced immunogenic cell death5,6, flavivirus infection and senescence. NKp46 recognition of the P domain of ecto-CRT triggers NKcell signalling and NKp46 caps with ecto-CRT in NK immune synapses. NKp46-mediated killing is inhibited by knockout or knockdownofCALR, the gene encoding CRT, or CRT antibodies, and is enhanced by ectopic expression of glycosylphosphatidylinositol-anchored CRT. NCR1)-deficient human (andNrc1-deficient mouse) NKcells are impaired in the killing of ZIKV-infected, ER-stressed and senescent cells and ecto-CRT-expressing cancer cells. Importantly, NKp46 recognition of ecto-CRT controls mouse B16 melanoma and RAS-driven lung cancers and enhances tumour-infiltrating NKcell degranulation and cytokine secretion. Thus, NKp46 recognition of ecto-CRT as a danger-associated molecular pattern eliminates ER-stressed cells.

Melanocytic Lesions with Peripheral Globules: Proposal of an Integrated Management Algorithm.

A peripheral rim of globules represents a marker of the horizontal growth phase in nevi and is a common feature in children and adolescents. The observation of melanocytic lesions with peripheral globules (MLPGs) in adulthood deserves more attention, since melanoma may exhibit this feature, albeit rarely. Risk-stratified management recommendations considering a global clinical approach are still missing. To analyze current knowledge on MLPGs and propose an integrated management algorithm stratified for age groups. We conducted a narrative review of current published data on MLPGs, analyzing clinical dermoscopic and confocal distinguishing features of melanoma from benign nevi. The risk of finding a melanoma when removing an MLPG increases with age, especially in people >55 years old, and is significantly higher in the extremities, head/neck and in case of a single asymmetrical lesion, ≥6 mm in diameter. Dermoscopic features associated with melanoma diagnosis include atypical peripheral globules, asymmetrical distribution, multiple rims as well as the reappearance of globules after prior loss. In addition, wide blue-grey regression areas, atypical networks, eccentric blotches, tan structureless peripheral areas and vascularization are atypical dermoscopic features. Confocal worrisome findings are represented by pagetoid cells within the epidermis, architectural disarrangement and atypical cells of the dermo-epidermal junction with irregular peripheral nests. We proposed a multi-step age-stratified management algorithm integrating clinical, dermoscopic and confocal findings that may increase the early recognition of melanoma and avoid surgical excision of benign nevi.

A Novel Multi-Task Learning Network Based on Melanoma Segmentation and Classification with Skin Lesion Images.

Melanoma is known worldwide as a malignant tumor and the fastest-growing skin cancer type. It is a very life-threatening disease with a high mortality rate. Automatic melanoma detection improves the early detection of the disease and the survival rate. In accordance with this purpose, we presented a multi-task learning approach based on melanoma recognition with dermoscopy images. Firstly, an effective pre-processing approach based on max pooling, contrast, and shape filters is used to eliminate hair details and to perform image enhancement operations. Next, the lesion region was segmented with a VGGNet model-based FCN Layer architecture using enhanced images. Later, a cropping process was performed for the detected lesions. Then, the cropped images were converted to the input size of the classifier model using the very deep super-resolution neural network approach, and the decrease in image resolution was minimized. Finally, a deep learning network approach based on pre-trained convolutional neural networks was developed for melanoma classification. We used the International Skin Imaging Collaboration, a publicly available dermoscopic skin lesion dataset in experimental studies. While the performance measures of accuracy, specificity, precision, and sensitivity, obtained for segmentation of the lesion region, were produced at rates of 96.99%, 92.53%, 97.65%, and 98.41%, respectively, the performance measures achieved rates for classification of 97.73%, 99.83%, 99.83%, and 95.67%, respectively.

Automated Malignant Melanoma Classification Using Convolutional Neural Networks

This research is proposed a design of architecture for melanoma (a kind of skin cancer) recognition by using a Convolutional Neural Network (CNN), work that will be useful for researchers in future projects in areas like biomedicine, machine learning, and others related moving forward with their studies and improving this proposal. CNN is mostly used in computer vision (a branch of artificial intelligence), applied to pattern recognition in skin moles and to determine the existence of malignant melanoma, or not, with a limited dataset. The CNN classifier designed and trained in this case was built through a couple of layers of convolution and pooling stacked to form a neural network of 6 layers followed by the fully connected to complete the architecture with an output classifier. The proposed database to train our CNN is the largest publicly collection of dermoscopic images of melanomas and other skin lesions, provided by the International Skin Imaging Collaboration (ISIC), sponsored by International Society for Digital Imaging of the Skin (ISDIS), an international effort to improve melanoma diagnosis. The purpose of this research was to design a Convolutional Neural Network with a high level of accuracy to help professionals in medicine with a melanoma diagnosis, in this case, it was possible to get accuracy up to 88.75 %.

Characteristics of the Structural Elements of the “Lines” in the Melanoma Recognition System

Characteristics of the Structural Elements of the “Lines” in the Melanoma Recognition System

A multi-stage melanoma recognition framework with deep residual neural network and hyperparameter optimization-based decision support in dermoscopy images

This paper developed a novel melanoma diagnosis model from dermoscopy images using a novel hybrid model. Melanoma is the most dangerous and rarest type of skin cancer. It is seen because of the uncontrolled proliferation of melanocyte cells that give color to the skin. Dermoscopy is a critical auxiliary diagnostic method in the differentiation of pigmented moles, which show moles by magnifying 10–20 times from skin cancers. This paper proposes a multi-stage melanoma recognition framework withskin lesion images obtained from dermoscopy. This model developed a practical pre-processing approach that includes dilation andpooling layers to remove hair details and reveal details in dermoscopy images. A deep residual neural network was then utilized as the feature extractor for processed images.Additionally, the Relief algorithm selected practical and distinctive features from these features. Finally, these selected features were fed to the input of the support vector machine (SVM) classifier. In addition, the Bayesian optimization algorithm was used for the optimum parameter selection of the SVM method. The International Skin Imaging Collaboration (ISIC-2019 and ISIC-2020) datasets were used to test the performance of the proposed model. As a result, the proposed model produced approximately 99% accuracy forclassifying melanoma or benign from skin lesion images. These results show that the proposed model can help physicians to automatically identify melanoma based on dermatological imaging.