| import dash
|
| from dash import html, dcc
|
| from dash import dash_table
|
| import pandas as pd
|
|
|
| dash.register_page(__name__, path="/", name="Introduction")
|
|
|
|
|
|
|
|
|
| paper_summary = """
|
| The Breast Cancer Wisconsin Diagnostic dataset originates from the work of
|
| W. Nick Street, William H. Wolberg, and O. L. Mangasarian (1992). Their study
|
| introduced an automated system for classifying breast tumors using
|
| fine-needle aspirated (FNA) cytology images.
|
|
|
| The system extracted quantitative characteristics of cell nuclei — such as radius,
|
| texture, smoothness, concavity, and symmetry — using active contour models
|
| (“snakes”) to trace boundaries of nuclei precisely. With these features, the authors
|
| used linear-programming-based classifiers and achieved cross-validation accuracy
|
| reaching **97%**, marking one of the earliest strong uses of machine learning in
|
| medical diagnosis.
|
| """
|
|
|
|
|
|
|
|
|
| feature_table = pd.DataFrame({
|
| "Feature Group": [
|
| "Radius", "Texture", "Perimeter", "Area", "Smoothness",
|
| "Compactness", "Concavity", "Concave Points",
|
| "Symmetry", "Fractal Dimension"
|
| ],
|
| "Description": [
|
| "Mean distance from center to perimeter",
|
| "Variation in pixel intensity",
|
| "Boundary length of the nucleus",
|
| "Size of the cell nucleus",
|
| "Local variation in radius",
|
| "Perimeter² / area",
|
| "Severity of concave regions",
|
| "Number of concave contour portions",
|
| "Nuclear symmetry",
|
| "Irregularity of contour"
|
| ]
|
| })
|
|
|
|
|
|
|
|
|
| CARD_STYLE = {
|
| "background": "white",
|
| "padding": "25px",
|
| "borderRadius": "12px",
|
| "boxShadow": "0 4px 12px rgba(0,0,0,0.1)",
|
| "marginBottom": "25px"
|
| }
|
|
|
| TITLE_STYLE = {
|
| "fontSize": "26px",
|
| "fontWeight": "600",
|
| "marginBottom": "15px",
|
| "color": "#333"
|
| }
|
|
|
| TEXT_STYLE = {
|
| "fontSize": "17px",
|
| "lineHeight": "1.6",
|
| "textAlign": "justify",
|
| "color": "#444"
|
| }
|
|
|
|
|
|
|
|
|
| layout = html.Div(
|
| style={
|
| "maxWidth": "950px",
|
| "margin": "auto",
|
| "padding": "30px"
|
| },
|
| children=[
|
|
|
|
|
|
|
|
|
| html.Div(
|
| style=CARD_STYLE,
|
| children=[
|
| html.H2("Introduction", style=TITLE_STYLE),
|
| html.P(paper_summary, style=TEXT_STYLE)
|
| ]
|
| ),
|
|
|
|
|
|
|
|
|
| html.Div(
|
| style=CARD_STYLE,
|
| children=[
|
| html.Img(
|
| src="/assets/celltumor.PNG",
|
| style={
|
| "width": "100%",
|
| "borderRadius": "10px",
|
| "boxShadow": "0 2px 10px rgba(0,0,0,0.15)"
|
| }
|
| ),
|
| html.P(
|
| "Figure: Example of cell nuclei boundaries from the original study.",
|
| style={"textAlign": "center", "fontStyle": "italic", "marginTop": "10px"}
|
| )
|
| ]
|
| ),
|
|
|
|
|
|
|
|
|
| html.Div(
|
| style=CARD_STYLE,
|
| children=[
|
| html.H3("Dataset Overview", style=TITLE_STYLE),
|
| html.P(
|
| """
|
| The Breast Cancer Wisconsin Diagnostic dataset contains 569 samples
|
| describing malignant and benign tumors. Each sample includes 30 numerical
|
| features derived from nuclear characteristics. The values are computed as:
|
| mean, standard error, and “worst case” (largest) measures.
|
| """,
|
| style=TEXT_STYLE
|
| ),
|
|
|
| dash_table.DataTable(
|
| data=feature_table.to_dict('records'),
|
| columns=[{"name": col, "id": col} for col in feature_table.columns],
|
| style_cell={
|
| "fontSize": "16px",
|
| "padding": "8px",
|
| "textAlign": "left"
|
| },
|
| style_header={
|
| "backgroundColor": "#fafafa",
|
| "fontWeight": "600",
|
| "fontSize": "17px",
|
| "borderBottom": "1px solid #ddd"
|
| },
|
| style_table={
|
| "marginTop": "20px",
|
| "boxShadow": "0 2px 8px rgba(0,0,0,0.1)",
|
| "borderRadius": "8px",
|
| "overflow": "hidden"
|
| }
|
| )
|
| ]
|
| )
|
| ]
|
| )
|
|
|
