diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000000000000000000000000000000000000..329345c0f9b6b0873c0751c5b181956b16254a32 --- /dev/null +++ b/.gitignore @@ -0,0 +1,3 @@ +env +__pycache__ +oral_cancer_model.h5 \ No newline at end of file diff --git a/Docerfile b/Docerfile new file mode 100644 index 0000000000000000000000000000000000000000..01a2100fa393ecddb2eb860554ef4ada4659dee1 --- /dev/null +++ b/Docerfile @@ -0,0 +1,13 @@ +FROM python:3.12.1 + +RUN useradd -m -u 1000 user +USER user +ENV PATH="/home/user/.local/bin:$PATH" + +WORKDIR /app + +COPY --chown=user ./requirements.txt requirements.txt +RUN pip install --no-cache-dir --upgrade -r requirements.txt + +COPY --chown=user . /app +CMD ["uvicorn", "app:app", "--host", "0.0.0.0", "--port", "7860"] \ No newline at end of file diff --git a/app.py b/app.py new file mode 100644 index 0000000000000000000000000000000000000000..f55c2d45cdbececac9b77509ce3448fe41931947 --- /dev/null +++ b/app.py @@ -0,0 +1,97 @@ +from fastapi import FastAPI, File, UploadFile, HTTPException +from fastapi.staticfiles import StaticFiles +from fastapi.middleware.cors import CORSMiddleware +import tensorflow as tf +from tensorflow import keras +import numpy as np +from PIL import Image +import io +from huggingface_hub import hf_hub_download + +app = FastAPI(title="Medical Image Classification API", version="1.0.0") + +# Add CORS middleware +app.add_middleware( + CORSMiddleware, + allow_origins=["*"], # Configure this appropriately for production + allow_credentials=True, + allow_methods=["*"], + allow_headers=["*"], +) + +weights_path = hf_hub_download( + repo_id="Rahul-Samedavar/OralCancer_Predictor", + filename="weights.h5", +) + +# Load model +base_model = tf.keras.applications.DenseNet121( + input_shape=(224, 224, 3), + include_top=False, + weights='imagenet' +) +base_model.trainable = False + +model = keras.models.Sequential() +model.add(base_model) +model.add(keras.layers.BatchNormalization()) +model.add(keras.layers.Flatten()) +model.add(keras.layers.Dense(1024, activation=tf.nn.relu, kernel_regularizer=keras.regularizers.l2(0.01))) +model.add(keras.layers.Dropout(.3)) +model.add(keras.layers.Dense(2, activation=tf.nn.softmax)) +model.load_weights(weights_path) + +class_names = ['Normal', 'OSCC'] + +def preprocess_image(image_bytes: bytes) -> np.ndarray: + """Preprocess image for model prediction""" + image = Image.open(io.BytesIO(image_bytes)).convert('RGB') + image = image.resize((224, 224)) + img_array = np.array(image) / 255.0 + return np.expand_dims(img_array, axis=0) + +@app.post("/predict") +async def predict(image: UploadFile = File(...)): + """Predict medical condition from uploaded image""" + + # Validate file type + if not image.content_type.startswith('image/'): + raise HTTPException(status_code=400, detail="File must be an image") + + try: + # Read image bytes + img_bytes = await image.read() + + # Preprocess image + processed_image = preprocess_image(img_bytes) + + # Make prediction + predictions = model.predict(processed_image)[0] + predicted_class = class_names[np.argmax(predictions)] + + # Format confidence scores + confidence = { + class_names[i]: float(f"{predictions[i]:.4f}") + for i in range(len(class_names)) + } + + return { + "predicted_class": predicted_class, + "confidence_scores": confidence + } + + except Exception as e: + raise HTTPException(status_code=500, detail=f"Error processing image: {str(e)}") + + +app.mount("/", StaticFiles(directory="static/main", html=True), name="main") +app.mount("/cnn/", StaticFiles(directory="static/cnn", html=True), name="cnn") + +@app.get("/health") +async def health(): + """Health check endpoint""" + return {"status": "healthy"} + +if __name__ == "__main__": + import uvicorn + uvicorn.run(app, host="0.0.0.0", port=8000) \ No newline at end of file diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 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0)||0)}snapToCursor(t){iD(e=>{let{drag:i}=this.getProps();if(!i1(e,i,this.currentDirection))return;let{projection:s}=this.visualElement,n=this.getAxisMotionValue(e);if(s&&s.layout){let{min:i,max:r}=s.layout.layoutBox[e];n.set(t[e]-tM(i,r,.5))}})}scalePositionWithinConstraints(){if(!this.visualElement.current)return;let{drag:t,dragConstraints:e}=this.getProps(),{projection:i}=this.visualElement;if(!iW(e)||!i||!this.constraints)return;this.stopAnimation();let s={x:0,y:0};iD(t=>{let e=this.getAxisMotionValue(t);if(e&&!1!==this.constraints){let i=e.get();s[t]=function(t,e){let i=.5,s=ix(t),n=ix(e);return n>s?i=er(e.min,e.max-s,t.min):s>n&&(i=er(t.min,t.max-n,e.min)),O(0,1,i)}({min:i,max:i},this.constraints[t])}});let{transformTemplate:n}=this.visualElement.getProps();this.visualElement.current.style.transform=n?n({},""):"none",i.root&&i.root.updateScroll(),i.updateLayout(),this.resolveConstraints(),iD(e=>{if(!i1(e,t,null))return;let 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t=this.visualElement.getProps(),{drag:e=!1,dragDirectionLock:i=!1,dragPropagation:s=!1,dragConstraints:n=!1,dragElastic:r=.35,dragMomentum:o=!0}=t;return{...t,drag:e,dragDirectionLock:i,dragPropagation:s,dragConstraints:n,dragElastic:r,dragMomentum:o}}}function i1(t,e,i){return(!0===e||e===t)&&(null===i||i===t)}class i2 extends ia{constructor(t){super(t),this.removeGroupControls=h,this.removeListeners=h,this.controls=new i0(t)}mount(){let{dragControls:t}=this.node.getProps();t&&(this.removeGroupControls=t.subscribe(this.controls)),this.removeListeners=this.controls.addListeners()||h}unmount(){this.removeGroupControls(),this.removeListeners()}}let i5=t=>(e,i)=>{t&&m.postRender(()=>t(e,i))};class i3 extends ia{constructor(){super(...arguments),this.removePointerDownListener=h}onPointerDown(t){this.session=new 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t=0;t.999999999999&&(e.x=1),e.y<1.0000000000001&&e.y>.999999999999&&(e.y=1)}}(this.layoutCorrected,this.treeScale,this.path,e),t.layout&&!t.target&&(1!==this.treeScale.x||1!==this.treeScale.y)&&(t.target=t.layout.layoutBox,t.targetWithTransforms=iE());let{target:a}=t;if(!a){this.prevProjectionDelta&&(this.createProjectionDeltas(),this.scheduleRender());return}this.projectionDelta&&this.prevProjectionDelta?(sP(this.prevProjectionDelta.x,this.projectionDelta.x),sP(this.prevProjectionDelta.y,this.projectionDelta.y)):this.createProjectionDeltas(),iw(this.projectionDelta,this.layoutCorrected,a,this.latestValues),this.treeScale.x===r&&this.treeScale.y===o&&sF(this.projectionDelta.x,this.prevProjectionDelta.x)&&sF(this.projectionDelta.y,this.prevProjectionDelta.y)||(this.hasProjected=!0,this.scheduleRender(),this.notifyListeners("projectionUpdate",a)),c.value&&sO.calculatedProjections++}hide(){this.isVisible=!1}show(){this.isVisible=!0}scheduleRender(t=!0){if(this.options.visualElement?.scheduleRender(),t){let 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0,this.resumingFrom.preserveOpacity=void 0);let t=this.getStack();t&&t.exitAnimationComplete(),this.resumingFrom=this.currentAnimation=this.animationValues=void 0,this.notifyListeners("animationComplete")}finishAnimation(){this.currentAnimation&&(this.mixTargetDelta&&this.mixTargetDelta(1e3),this.currentAnimation.stop()),this.completeAnimation()}applyTransformsToTarget(){let t=this.getLead(),{targetWithTransforms:e,target:i,layout:s,latestValues:n}=t;if(e&&i&&s){if(this!==t&&this.layout&&s&&s7(this.options.animationType,this.layout.layoutBox,s.layoutBox)){i=this.target||iE();let e=ix(this.layout.layoutBox.x);i.x.min=t.target.x.min,i.x.max=i.x.min+e;let s=ix(this.layout.layoutBox.y);i.y.min=t.target.y.min,i.y.max=i.y.min+s}sb(e,i),iU(e,n),iw(this.projectionDeltaWithTransform,this.layoutCorrected,e,n)}}registerSharedNode(t,e){this.sharedNodes.has(t)||this.sharedNodes.set(t,new sB),this.sharedNodes.get(t).add(e);let i=e.options.initialPromotionConfig;e.promote({transition:i?i.transition:void 0,preserveFollowOpacity:i&&i.shouldPreserveFollowOpacity?i.shouldPreserveFollowOpacity(e):void 0})}isLead(){let t=this.getStack();return!t||t.lead===this}getLead(){let{layoutId:t}=this.options;return t&&this.getStack()?.lead||this}getPrevLead(){let{layoutId:t}=this.options;return t?this.getStack()?.prevLead:void 0}getStack(){let{layoutId:t}=this.options;if(t)return this.root.sharedNodes.get(t)}promote({needsReset:t,transition:e,preserveFollowOpacity:i}={}){let s=this.getStack();s&&s.promote(this,i),t&&(this.projectionDelta=void 0,this.needsReset=!0),e&&this.setOptions({transition:e})}relegate(){let t=this.getStack();return!!t&&t.relegate(this)}resetSkewAndRotation(){let{visualElement:t}=this.options;if(!t)return;let e=!1,{latestValues:i}=t;if((i.z||i.rotate||i.rotateX||i.rotateY||i.rotateZ||i.skewX||i.skewY)&&(e=!0),!e)return;let s={};i.z&&s$("z",t,s,this.animationValues);for(let 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sZ(t){t.finishAnimation(),t.targetDelta=t.relativeTarget=t.target=void 0,t.isProjectionDirty=!0}function sQ(t){t.resolveTargetDelta()}function sJ(t){t.calcProjection()}function s0(t){t.resetSkewAndRotation()}function s1(t){t.removeLeadSnapshot()}function s2(t,e,i){t.translate=tM(e.translate,0,i),t.scale=tM(e.scale,1,i),t.origin=e.origin,t.originPoint=e.originPoint}function s5(t,e,i,s){t.min=tM(e.min,i.min,s),t.max=tM(e.max,i.max,s)}function s3(t){return t.animationValues&&void 0!==t.animationValues.opacityExit}let s9={duration:.45,ease:[.4,0,.1,1]},s4=t=>"undefined"!=typeof navigator&&navigator.userAgent&&navigator.userAgent.toLowerCase().includes(t),s6=s4("applewebkit/")&&!s4("chrome/")?Math.round:h;function s8(t){t.min=s6(t.min),t.max=s6(t.max)}function s7(t,e,i){return"position"===t||"preserve-aspect"===t&&!(.2>=Math.abs(sj(e)-sj(i)))}function nt(t){return t!==t.root&&t.scroll?.wasRoot}let 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Convolutional Neural Network Visualized

Explore how CNNs process images through multiple layers of abstraction

Input Image

The CNN process begins with a raw input image. For our example, we'll use a handwritten digit '5'. The image is represented as a matrix of pixel values.

Handwritten digit 5
Raw Input Image

Digital Representation

Computers see images as arrays of numbers. Each pixel is represented as a value between 0 (black) and 255 (white) for grayscale images.

Matrix Representation
[
  [0, 0, 0, 0, 0, 0, 0, 0],
  [0, 0, 110, 190, 253, 70, 0, 0],
  [0, 0, 191, 40, 0, 191, 0, 0],
  [0, 0, 160, 0, 0, 120, 0, 0],
  [0, 0, 127, 195, 210, 20, 0, 0],
  [0, 0, 0, 0, 40, 173, 0, 0],
  [0, 0, 75, 60, 20, 230, 0, 0],
  [0, 0, 90, 230, 180, 35, 0, 0]
]

Convolution Operation

The convolution operation slides a filter (kernel) across the input image to detect features like edges, textures, or patterns.

Kernel Sliding

Kernel/Filter

-1
-1
-1
-1
8
-1
-1
-1
-1

Edge detection filter

Feature Map

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Resulting feature map from convolution operation

ReLU Activation

The Rectified Linear Unit (ReLU) introduces non-linearity to the network by converting all negative values to zero, allowing the network to learn complex patterns.

Before ReLU

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Feature map contains both positive and negative values

f(x) = max(0, x)
Negative values
Positive values

After ReLU

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Negative values are replaced with zeros, introducing non-linearity

Why Non-Linearity Matters

Without non-linear activation functions like ReLU, the neural network would only be able to learn linear relationships in the data, significantly limiting its ability to solve complex problems. ReLU enables the network to model more complex functions while being computationally efficient.

Pooling Layer

Pooling reduces the spatial dimensions of the feature maps, preserving the most important information while reducing computation and preventing overfitting.

Max Pooling (2×2 Window)

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Max Pooling

For each 2×2 window, keep
only the maximum value

Pooled Feature Map

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Benefits of Pooling

  • Reduces spatial dimensions by 75%
  • Preserves important features
  • Makes detection more robust to position
  • Reduces overfitting

Deep Layer Abstraction

As we progress through deeper layers of the CNN, the network learns increasingly abstract representations of the input image, from simple edges to complex shapes and patterns.

Layer 1: Edges & Corners

Layer 2: Simple Shapes

Layer 3: Complex Features

Hierarchy of Features

Early Layers (e.g., Layer 1)

Detect low-level features like edges, corners, and basic textures. These are the building blocks for more complex pattern recognition.

Middle Layers (e.g., Layer 2)

Combine edges and textures into more complex patterns and shapes like circles, squares, and simple object parts.

Deep Layers (e.g., Layer 3)

Recognize complex, high-level concepts specific to the training dataset, such as eyes, faces, or entire objects.

Flattening and Fully Connected Layer

The final stage of a CNN involves flattening the feature maps into a single vector and passing it through fully connected layers to make predictions.

Feature Maps to Vector

Flattening

The 2D feature maps are converted into a 1D vector by arranging all the values in a single row. This allows the network to transition from convolutional layers to fully connected layers.

Fully Connected Network

Prediction: "5"

Class
Probability
0
1%
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2%
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3%
3
5%
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8%
5
65%
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5%
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4%
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5%
9
2%

Learning via Backpropagation

The CNN learns by comparing its predictions with the true labels, calculating the error, and then propagating this error backward through the network to update weights.

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Error: 0.42
Update weights
Update weights
Forward Pass
Calculate Error
Backward Pass

Backpropagation

Backpropagation calculates how much each neuron's weight contributed to the output error. It then adjusts these weights to minimize the error in future predictions, using the chain rule of calculus to distribute error responsibility throughout the network.

Gradient Descent

The network uses gradient descent to adjust weights in the direction that reduces error. By repeatedly processing many examples and making small weight updates, the model gradually improves its ability to recognize patterns and make accurate predictions.

Key CNN Components Recap

Input Layer
Convolutional Layers
Activation Functions
Pooling Layers
Fully Connected Layers
Output Layer
Backpropagation

© 2025 CNN Visualizer

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Diagnosis Steps

The AI-powered oral cancer diagnosis process follows a systematic workflow from image acquisition to final report generation. Each step is optimized for accuracy and efficiency.

Tissue Collection

Biopsy samples are collected from the epithelial lining of the oral cavity for further analysis.

Microscopic Imaging

Tissue samples are examined under an electronic microscope to produce high-resolution histopathological images.

Histopathology Imaging

The observed cellular structures are digitally captured as histopathological images for diagnostic evaluation.

Image Digitization

These images are digitized and fed into the computer system, preprocessed for consistency and clarity.

CNN Processing

A convolutional neural network analyzes the image, extracting features indicative of cancerous changes.

Diagnosis Output

The model outputs a classification—benign or malignant—with a confidence score, aiding medical diagnosis.

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AI in Oral Cancer Diagnosis

Early detection through intelligent imaging

Try Prediction

About Oral Cancer

Oral cancer is a significant global health concern, characterized by the uncontrolled growth of cells in the oral cavity. It typically manifests as a persistent lesion in the mouth that doesn't heal within two weeks. The most common form is squamous cell carcinoma, which accounts for over 90% of all oral malignancies.

Risk factors include tobacco use (smoking and smokeless), excessive alcohol consumption, human papillomavirus (HPV) infection, and prolonged sun exposure (for lip cancers). Early detection is crucial, often requiring a biopsy for definitive diagnosis.

In countries like India, oral cancer is particularly prevalent, accounting for approximately 30% of all cancers. This high incidence is attributed to widespread tobacco and betel quid chewing habits. The five-year survival rate drops dramatically from 83% for localized cases to just 32% for cases where cancer has spread, highlighting the importance of early detection.

Early stages of oral cancer may present as white or red patches (leukoplakia or erythroplakia) that can progress to dysplasia and eventually invasive carcinoma if left untreated. Traditional diagnostic methods rely heavily on clinical examination followed by histopathological analysis, which can be time-consuming and subjective.

AI in Diagnosis

Convolutional neural networks (CNNs) have revolutionized medical image analysis by automatically extracting relevant features from oral cavity images. These networks consist of multiple layers that progressively learn to identify patterns associated with cancerous and pre-cancerous conditions.

Input

High-resolution images of oral tissue are fed into the model

Feature Extraction

The CNN identifies key visual patterns using activation functions

Classification

The classifier determines the probability of malignancy

AI-powered diagnosis offers several advantages over traditional methods:

  • Reduced subjectivity and inter-observer variability
  • Faster results, enabling quicker treatment decisions
  • Potential for earlier detection of subtle changes invisible to the human eye
  • Accessibility in regions with limited access to pathologists
  • Quantitative assessment of disease progression

By combining the expertise of healthcare professionals with the analytical power of AI, we can significantly improve early detection rates and patient outcomes in oral cancer cases.

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AI Prediction Tool

Upload an image of oral tissue to receive an AI-powered diagnosis. This tool demonstrates how our convolutional neural network analyzes medical images to detect potential signs of oral cancer.

Drag and drop an image here, or click to select

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Real-World Applications

AI technologies for oral cancer diagnosis are being implemented in various healthcare settings around the world. Here are some notable real-world applications that demonstrate the practical impact of these innovations.

Jamia Millia Islamia (JMI), India

JMI researchers developed a pioneering AI-powered system and the world’s first comprehensive oral cancer histology image database (ORCHID) to enhance the diagnosis and prediction of oral malignant disorders. Their patented method uses AI and digital pathology to analyze over 300,000 high-resolution tissue images, accurately identifying conditions such as oral submucous fibrosis (OSMF) and oral squamous cell carcinoma (OSCC), and predicting the progression of pre-malignant lesions to cancer.

SPARSH & IISc: Portable AI Biopsy Tool

SPARSH Hospital, in partnership with IISc and GE Healthcare Innovation Centre, has implemented AI-driven solutions across the oral cancer care continuum, including early screening and virtual surgical planning. Their collaborative approach leverages deep learning to analyze intraoral images and digital pathology slides.

IIIT-Hyderabad and Biocon Foundation

IIIT-Hyderabad and Biocon Foundation have focused on developing and validating deep learning models like DenseNet201 and Swin Transformer for classifying intraoral images as suspicious or non-suspicious for oral cancer. Using large, diverse datasets and explainability tools like GradCAM, their models highlight the importance of robust, explainable AI tools.

Dr. Chao’s Lab (Canada)

Dr. Chao’s Lab in Canada is developing AI algorithms for oral cancer diagnosis, focusing on transformer-based models and explainable AI techniques for digital pathology and multi-modal data integration. Their work supports global efforts to standardize and improve AI-driven screening.

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