[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125631-en":3,"doc-seo-125631-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125631,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",7,"Healthcare","Detecting Retinal Damage with Image Analysis using Machine learning Algorithm","Retina plays a critical role in transforming focused light into neural signals that support vision, and retinal detachment (RD) can lead to severe impairment or blindness. Identifying retinal damage is difficult because relevant layers and nerve connections are delicate and easily confused with other conditions. This work proposes IMRCNN, an improved mask recurrent convolutional neural network, for RD detection and lesion classification. Performance is assessed using sensitivity, accuracy, specificity, and F-score on 54,000 retinographic images, achieving 92.20%, 95.10%, 98%, and 93% average values. The method improves RD lesion classification and enables intensity-level analysis through pixel-wise bottom-up image analysis.","Appl. Math. Inf. Sci. 17, No. 3, 395-403 (2023)  395  \n\n| Applied Mathematics and Information Sciences An International Journal |  |\n| --- | --- |\n| [http://dx.doi.org/10.18576/amis/170301](http://dx.doi.org/10.18576/amis/170301)\u003Cbr>Detecting Retinal Damage with Image Analysis using Machine learning Algorithm\u003Cbr>Anitha. E1 ,2 ,􀀃 andA. Antonidoss 2\u003Cbr>1Loyola-ICAM College of Engineering and Technology, Nungambakkam, India\u003Cbr>2Computer Science and Engineering and Hindustan Institute of Technology and Science, Padur, Chennai, India\u003Cbr>Received: 2 Jan. 2023, Revised: 22 Feb. 2023, Accepted: 5 Apr. 2023\u003Cbr>Published online: 1 May 2023 |  |\n| Abstract: Retina is an important layer of tissue in the back of the eye. The retina’s primary job is to gather light that the lens has focused, convert the light into neural signals, and send those signals to the brain for optical compensation. This crucial tissue may appear damaged due to retinal detachment (RD) . Such conditions can impair vision and undoubtedly have the potential to be severe enough to cause blindness. It can be challenging to identify the damage since the layers and the nerve connections are too delicate and thin, and they may be mistaken for another illness. IMRCNN, an improved mask recurrent convolutional neural network, was proposed to test its ef􀀂ciency in RD detection. The effectiveness of the suggested approach is evaluated by looking at the implementation measures Sensitivity (S), Accuracy (A), Speci􀀂city (SP), and F-score (F). For 54,000 retinographic images, the average S, SP, A, and F values were 92.20%, 98%, 95.10%, and 93%, respectively. This suggested model performs better at classifying RD-related lesions and classifying the intensity levels on various retinal pictures. This type of analysis methodology concentrates on breaking down images into pixels and studying the data from the bottom up. It offers greater analysis and more precisely detects RD. This study presents essential knowledge and cutting-edge machine learning methodologies in the 􀀂eld of medical image processing methods and analysis. The main objectives of this work are to de􀀂ne and apply the identi􀀂ed and addressed important principles as well as to provide research on medical image processing.\u003Cbr>Keywords: Retinal detachment, Improved mask recurrent convolutional neural network, Retinographic images, Medical image processing |  |\n| 1 Introduction\u003Cbr>The retina is the membrane of sensory receptors within the internal surfaces of the eyeball’s posterior portion. Several layers of cells, including these specialized cells known as photoreceptors, are utilized in the formation of the retina. There are two categories of photoreceptors in the human eye rods and cones [1] . The rod photoreceptors discover signals, offer black-and-white perception, and perform effectively in little illumination. Cones are important for trichromacy and visible pleasure and accomplish well in standard and brilliant lighting. As rods are inserted in the retina, cones are focused on themacula, a small, central region of the retina. The fovea, or minor depression found in the macula, can be found at its center shown in Figure 1 . The fovea is the part of theretina with the highest density of cone photoreceptors and | is responsible for maximum sharp-sightedness and vision [2] .\u003Cbr>Retina Functions: Photoreceptor cells sense intense mild emitted from the cornea and lens. It then converts that mild into chemical and frightened indicators that are transported to the brain through nerve optics. They are transformed into pixels and visible impressions when they reach the cortical region of the brain [3] .\u003Cbr>Retina Problems:\u003Cbr>1.Macular degeneration-an illness that abolishes your acute and vital vision\u003Cbr>2.Diabetic eye infection\u003Cbr>3.Retinal objectivity - a medical substitute, when theretina is hauled aside from the following of the eye\u003Cbr>4.Retinoblastoma-cancer of the retina. It is an utmost joint in young youngsters.\u003Cbr>5.Mac","cbCaihYyGfX4hWU9","https://ap.wps.com/l/cbCaihYyGfX4hWU9","pdf",541756,1,9,"English","en",105,"# Abstract\n# Introduction\n## Retina Functions\n## Retina Problems\n# Related Work","[{\"question\":\"What is the main goal of this study?\",\"answer\":\"To detect retinal detachment (RD) by using image analysis with an improved mask recurrent convolutional neural network (IMRCNN), enabling better lesion classification and intensity-level understanding.\"},{\"question\":\"Why is RD detection challenging?\",\"answer\":\"Relevant retinal layers and nerve connections are thin and delicate, making damage identification difficult and prone to confusion with other illnesses.\"},{\"question\":\"How is the proposed model evaluated?\",\"answer\":\"The approach is evaluated using sensitivity, accuracy, specificity, and F-score on 54,000 retinographic images.\"}]","Detecting Retinal Damage with Image Analysis using Machine learning Algorithm | PDF",1785900317,23,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"detecting-retinal-damage-with-image-analysis-using-machine-learning-algorithm","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/detecting-retinal-damage-with-image-analysis-using-machine-learning-algorithm/125631/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of this study?","Question",{"text":75,"@type":76},"To detect retinal detachment (RD) by using image analysis with an improved mask recurrent convolutional neural network (IMRCNN), enabling better lesion classification and intensity-level understanding.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Why is RD detection challenging?",{"text":80,"@type":76},"Relevant retinal layers and nerve connections are thin and delicate, making damage identification difficult and prone to confusion with other illnesses.",{"name":82,"@type":73,"acceptedAnswer":83},"How is the proposed model evaluated?",{"text":84,"@type":76},"The approach is evaluated using sensitivity, accuracy, specificity, and F-score on 54,000 retinographic images.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,127,130,134],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]