[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-371368-105":59,"doc-detail-371368-en":130},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":123,"head_meta":125,"extra_data":127,"updated_unix":129},105,"en","a-hybrid-parallel-convolutional-spiking-neural-network-for-enhanced-skin-cancer-detection","A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection","","A hybrid parallel convolutional spiking neural network (PCSN-Net) is proposed for reliable skin cancer detection. Skin cancer images are denoised using a Medav filter, then affected regions are segmented with DeepSegNet by integrating SegNet and deep joint segmentation fused via RV coefficient. Segmented outputs are augmented with geometric and color-space transformations, mixup, and CutMix. Texture and statistical descriptors based on DWT-LDP with entropy and LNDP are extracted, and fused to classify malignant versus benign lesions. Experiments report 95.7% accuracy, 94.7% sensitivity, 92.6% specificity, with FPR 10.7% and PPV 90.8%, outperforming EfficientNet, DenseNet, and Inception-ResNet-V2 while supporting feasible real-time clinical use.",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/a-hybrid-parallel-convolutional-spiking-neural-network-for-enhanced-skin-cancer-detection/371368/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/a-hybrid-parallel-convolutional-spiking-neural-network-for-enhanced-skin-cancer-detection/371368.png","ImageObject",300,407,{"name":92,"@type":93},"Genevieve","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-23",true,{"@type":102,"interactionType":103,"userInteractionCount":39},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"What does PCSN-Net aim to achieve for skin cancer detection?","Question",{"text":112,"@type":113},"PCSN-Net targets accurate and dependable classification of malignant and benign skin lesions, including improved discrimination with clinically useful performance.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How are input images prepared before classification?",{"text":117,"@type":113},"Images are preprocessed using a Medav filter to remove noise, then segmented using DeepSegNet to isolate affected regions.",{"name":119,"@type":110,"acceptedAnswer":120},"What performance does the proposed method report compared with other models?",{"text":121,"@type":113},"The method reports 95.7% accuracy, 94.7% sensitivity, and 92.6% specificity, with FPR 10.7% and PPV 90.8%, outperforming models such as EfficientNet, DenseNet, and Inception-ResNet-V2.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},371368,1790598019,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":39,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145},1374391974585,"https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c","[www. nature.com/scientificreports](www. nature.com/scientificreports)  \nOPEN  \nA hybrid parallel convolutional spiking neural network for enhanced skin cancer detection  \nK. Anup Kumar1,2 & C. Vanmathi1,2􀀍  \nThe most widespread kind of cancer, affecting millions of lives is skin cancer. When the condition of illness worsens, the chance of survival is reduced, and thus detection of skin cancer is extremely difficult. Hence, this paper introduces a new model, known as Parallel Convolutional Spiking Neural Network (PCSN-Net) for detecting skin cancer. Initially, the input skin cancer image is pre-processed by employing Medav filter to eradicate the noise in image. Next, affected region is segmented by utilizing DeepSegNet, which is formed by integrating SegNet and Deep joint segmentation, where RV coefficient is used to fuse the outputs. Here, the segmented image is then augmented by including process, such as geometric transformation, colorspace transformation, mixing images Pixel averaging (mixup), and overlaying crops (CutMix). Then textural, statistical, Discrete Wavelet Transform (DWT) based Local Direction Pattern (LDP) with entropy, and Local Normal Derivative Pattern (LNDP) features are mined. Finally, skin cancer detection is executed using PCSN-Net, which is formed by fusing Parallel Convolutional Neural Network (PCNN) and Deep Spiking Neural Network (DSNN) . In this work, the suggested PCSN-Net system shows high accuracy and reliability in identifying skin cancer. The experimental findings suggest that PCSN-Net has an accuracy of 95.7%, a sensitivity of 94.7%, and a specificity of 92.6%. These parameters demonstrate the model’s capacity to discriminate among malignant and benign skin lesions properly. Furthermore, the system has a false positive rate (FPR) of 10.7% and a positive predictive value (PPV) of 90.8%, demonstrating its capacity to reduce wrong diagnosis while prioritizing true positive instances. PCSN-Net outperforms various complex algorithms, including EfficientNet, DenseNet, and Inception-ResNet-V2, despite preserving effective training and inference times. The results obtained show the feasibility of the model for real-time clinical use, strengthening its capacity for quick and accurate skin cancer detection.  \nKeywords Parallel convolutional neural network, Deep spiking neural network, SegNet, Deep joint segmentation, RV coefficient  \nSkin cancer is increasing development of skin cells because of inherited or alteration irregularity of damaged Deoxyribonucleic Acid (DNA). Generally. Skin cancer is fatal of all other types of cancerous cells. According to the World Health Organization (WHO), about 1/3 of total cancer cases are determined globally are skin cancer1. Generally, the malignant stage of skin cancer is caused due to pigmented cells termed melanocytes2. Skin cancer is commonly found in different colors, like royal purple, azure, and rosy pink, or it can also be colorless. The rapid spreading ability of skin cancer makes it more fatal and dangerous than other cancers3. Skin cancer mainly occurs due to several hereditary and environmental factors, namely the exposure of harmful Ultraviolet (UV) radiation from the sun, particularly short wavelength of UV-B and long wavelength of UV-A. These radiations from the sun lead to an increase in the growth of the melanocytes, which are the pigment generating cells of human skin. The most commonly reported skin cancerous cells are Squamous Cell Carcinoma (SCC), Basal Cell Carcinoma (BCC), and malignant melanoma4. The SCC and BCC are non-melanocytic cancers among all these different skin cancers, which are also considered innocuous. Similarly, malignant melanoma is considered the most life-threatening skin cancer with increasing death cases1.  \nMalignant melanoma is grave kind of skin cancer and the exact underlying reason of malignant melanoma is unknown. However, various factors, like environmental contact, ultraviolet radiation, genetic factors, and soon ","cbCaisYWcfpuIRm3","https://ap.wps.com/l/cbCaisYWcfpuIRm3","pdf",4988245,22,"English","# Method Overview\n## Preprocessing and Denoising\n## Segmentation with DeepSegNet\n## Data Augmentation\n## Feature Extraction\n## PCSN-Net Classification and Results","[{\"question\":\"What does PCSN-Net aim to achieve for skin cancer detection?\",\"answer\":\"PCSN-Net targets accurate and dependable classification of malignant and benign skin lesions, including improved discrimination with clinically useful performance.\"},{\"question\":\"How are input images prepared before classification?\",\"answer\":\"Images are preprocessed using a Medav filter to remove noise, then segmented using DeepSegNet to isolate affected regions.\"},{\"question\":\"What performance does the proposed method report compared with other models?\",\"answer\":\"The method reports 95.7% accuracy, 94.7% sensitivity, and 92.6% specificity, with FPR 10.7% and PPV 90.8%, outperforming models such as EfficientNet, DenseNet, and Inception-ResNet-V2.\"}]","A hybrid parallel convolutional spiking neural network for enhanced skin cancer detection | PDF",1790191911,55]