K. Kalyani¹, Sara A Althubiti², Mohammed Altaf Ahmed³, E. Laxmi Lydia⁴, Seifedine Kadry⁵, Neunggyu Han⁶, Yunyoung Nam^6,*

CMC-Computers, Materials & Continua, Vol.75, No.1, pp. 149-164, 2023, DOI:10.32604/cmc.2023.033005

Abstract Melanoma is a skin disease with high mortality rate while early diagnoses of the disease can increase the survival chances of patients. It is challenging to automatically diagnose melanoma from dermoscopic skin samples. Computer-Aided Diagnostic (CAD) tool saves time and effort in diagnosing melanoma compared to existing medical approaches. In this background, there is a need exists to design an automated classification model for melanoma that can utilize deep and rich feature datasets of an image for disease classification. The current study develops an Intelligent Arithmetic Optimization with Ensemble Deep Transfer Learning Based Melanoma Classification (IAOEDTT-MC) model. The proposed IAOEDTT-MC… More >

G. Murugesan^1,*, J. Jeyapriya², M. Hemalatha³, S. Rajeshkannan⁴

Intelligent Automation & Soft Computing, Vol.36, No.2, pp. 2099-2109, 2023, DOI:10.32604/iasc.2023.032068

Abstract The diagnostic interpretation of dermoscopic images is a complex task as it is very difficult to identify the skin lesions from the normal. Thus the accurate detection of potential abnormalities is required for patient monitoring and effective treatment. In this work, a Two-Tier Segmentation (TTS) system is designed, which combines the unsupervised and supervised techniques for skin lesion segmentation. It comprises preprocessing by the median filter, TTS by Colour K-Means Clustering (CKMC) for initial segmentation and Faster Region based Convolutional Neural Network (FR-CNN) for refined segmentation. The CKMC approach is evaluated using the different number of clusters (k = 3,… More >

Roberta Costanzo¹, Vishal Parmar², Salvatore Marrone¹, Domenico Gerardo Iacopino¹, Giovanni Federico Nicoletti³, Giuseppe Emmanuele Umana⁴, Gianluca Scalia^3,*

Oncologie, Vol.24, No.4, pp. 937-942, 2022, DOI:10.32604/oncologie.2022.027155

Abstract Primary intracranial melanomas are rare, with a challenging diagnosis based only on clinical and imaging features. The authors described the case of an intracerebral right parieto-temporal melanoma mimicking a cavernoma in a patient affected by Crohn’s disease. A 67-year-old female patient with Crohn’s disease and small bowel stenosis was hospitalized for surgical removal of the terminal ileum and latero-lateral ileo-colic anastomosis. During postoperative week 1, the patient developed psychomotor agitation followed by altered consciousness. An urgent brain CT showed a right intracerebral parieto-temporal hemorrhage with intralesional calcifications. The patient underwent a decompressive craniectomy with hematoma drainage and a complete resection… More >

NADEZHDA PALKINA, EKATERINA SERGEEVA, TATIANA RUKSHA^*

Oncology Research, Vol.29, No.6, pp. 393-400, 2021, DOI:10.32604/or.2022.025816

Abstract Melanoma is one of the most aggressive types of malignant tumors, commonly affecting young individuals. The treatment of metastatic tumors remains obscure due to the resistance of tumor cells to drugs mediated by various mechanisms. The acquisition of a resistant phenotype is associated with both genetic and epigenetic alterations in cancer cells. Therefore, the current study aimed to investigate whether microRNA (miR)-204-5p could promote alterations in the cell cycle and apoptosis of dacarbazine (DTIC)-treated melanoma cells. Quantitative real time PCR showed that transfection of DTIC-treated SK-MEL-2 melanoma cells with miR-204-5p mimics significantly upregulated miR-204-5p. However, flow cytometric analysis revealed that… More >

Amani Abdulrahman Albraikan¹, Nadhem NEMRI², Mimouna Abdullah Alkhonaini³, Anwer Mustafa Hilal^4,*, Ishfaq Yaseen⁴, Abdelwahed Motwakel⁴

CMC-Computers, Materials & Continua, Vol.74, No.2, pp. 2443-2459, 2023, DOI:10.32604/cmc.2023.026379

Abstract Melanoma remains a serious illness which is a common form of skin cancer. Since the earlier detection of melanoma reduces the mortality rate, it is essential to design reliable and automated disease diagnosis model using dermoscopic images. The recent advances in deep learning (DL) models find useful to examine the medical image and make proper decisions. In this study, an automated deep learning based melanoma detection and classification (ADL-MDC) model is presented. The goal of the ADL-MDC technique is to examine the dermoscopic images to determine the existence of melanoma. The ADL-MDC technique performs contrast enhancement and data augmentation at… More >

FAISAL HASSAN TOBEIGEI¹, REEM M. GAHTANI², AHMAD SHAIKH², AMER AL ALI³, NADER KAMELI^4,5, HOSSAM KAMLI², PRASANNA RAJAGOPALAN^2,6,*

Oncology Research, Vol.29, No.5, pp. 305-318, 2021, DOI:10.32604/or.2022.025187

Abstract Malignant melanoma is characterized by both genetic and molecular alterations that activate phosphoinositide 3-kinase (PI3K), and RAS/BRAF pathways. In this work, through diversity-based high-throughput virtual screening we identified a lead molecule that selectively targets PI3K and BRAF^V600E kinases. Computational screening, Molecular dynamics simulation and MMPBSA calculations were performed. PI3K and BRAF^V600E kinase inhibition was done. A375 and G-361 cells were used for in vitro cellular analysis to determine antiproliferative effects, annexin V binding, nuclear fragmentation and cell cycle analysis. Computational screening of small molecules indicates compound CB-006-3 selectively targets PI3KCG (gamma subunit), PI3KCD (delta subunit) and BRAF^V600E. Molecular dynamics simulation… More >

S. Gopikha^*, M. Balamurugan

Computer Systems Science and Engineering, Vol.44, No.3, pp. 2727-2742, 2023, DOI:10.32604/csse.2023.028609

Abstract Melanoma is the most lethal malignant tumour, and its prevalence is increasing. Early detection and diagnosis of skin cancer can alert patients to manage precautions and dramatically improve the lives of people. Recently, deep learning has grown increasingly popular in the extraction and categorization of skin cancer features for effective prediction. A deep learning model learns and co-adapts representations and features from training data to the point where it fails to perform well on test data. As a result, overfitting and poor performance occur. To deal with this issue, we proposed a novel Consecutive Layerwise weight Constraint MaxNorm model (CLCM-net)… More >

Saleem Mustafa¹, Arfan Jaffar¹, Muhammad Waseem Iqbal^2,*, Asma Abubakar², Abdullah S. Alshahrani³, Ahmed Alghamdi⁴

Intelligent Automation & Soft Computing, Vol.35, No.2, pp. 2205-2218, 2023, DOI:10.32604/iasc.2023.029549

Abstract Melanoma is of the lethal and rare types of skin cancer. It is curable at an initial stage and the patient can survive easily. It is very difficult to screen all skin lesion patients due to costly treatment. Clinicians are requiring a correct method for the right treatment for dermoscopic clinical features such as lesion borders, pigment networks, and the color of melanoma. These challenges are required an automated system to classify the clinical features of melanoma and non-melanoma disease. The trained clinicians can overcome the issues such as low contrast, lesions varying in size, color, and the existence of… More >

Yankun Zhang^*, Wei Qian^*, Feng Feng^†, Qian Cao^*, Yanqi Li^*, Ying Hou^*, Luyang Zhang^*, Jufeng Fan^*

Oncology Research, Vol.27, No.3, pp. 371-377, 2019, DOI:10.3727/096504018X15178740729367

Abstract This study aimed to investigate the effect and underlying mechanism of lncRNA CASC2 in malignant melanoma (MM). Expression of CASC2 in MM tissues and cells was detected. A375 cells were transfected with pc-CASC2, si-CASC2, miR-18a-5p inhibitor, or corresponding controls, and then cell proliferation, migration, and invasion were detected using MTT assay, colony formation assay, and Transwell analysis, respectively. The relationship of miR-18a-5p and CASC2 or RUNX1 was detected by luciferase reporter assay. The levels of CASC2 and RUNX1 were significantly reduced in MM tissues compared with normal skin tissues or cells, while the miR-18a-5p level was obviously increased (all p<0.01).… More >

Xiyan Sun^*1, Chao Zhang^†1, Yang Cao^†, Erbiao Liu^*

Oncology Research, Vol.27, No.3, pp. 317-323, 2019, DOI:10.3727/096504018X15228863026239

Abstract miR-150 has been demonstrated to inhibit tumor progression in various human cancers, including colorectal cancer, ovarian cancer, and thyroid cancer. However, the role of miR-150 in melanoma remains to be determined. In this study, we found that miR-150 was underexpressed in melanoma tissues and cell lines. Through transfection of miR-150 mimics, we found that miR-150 significantly inhibited the proliferation, migration, and invasion of melanoma cells. In mechanism, we found that MYB was a target of miR-150 in melanoma cells. Overexpression of miR-150 significantly inhibited mRNA and protein levels of MYB in melanoma cells. Moreover, there was an inverse correlation between… More >