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Fixels

Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.

Technology

In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.

Presentation and report

See our presentation here, and our report here.

Other contributors

Fernando Julio Cendra

Edward Suryajaya

About

A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction

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, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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Used by
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, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
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Used by
Contributors
Languages

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Fixels
Fixels is an application that aims to give quick diabetic retinopathy prediction based on retinal images. We trained a convolutional neural network on top of kaggle public datasets and messidor private datasets. The trained model then became nothing but the heart of our application.
Technology
In fixels, we use a python server with all the machine learning related modules including keras and tensorflow to train our model and generate prediction based on that model. For the client-side, it's a simple web application built using react and typescript for now. We are planning to develop the mobile version in the future.
Presentation and report
See our presentation here, and our report here.
Other contributors
Fernando Julio Cendra
Edward Suryajaya
About
A machine learning enabled system to generate quick, accurate, and reliable diabetic retinopathy prediction
Resources
Readme
Activity
Stars
0 stars
Watchers
1 watching
Forks
1 fork
Report repository
Releases
Packages
Used by
Contributors
Languages