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+<div class="sc-cmRAlD dkqmWS"><div class="sc-UEtKG dGqiYy sc-flttKd cguEtd"><div class="sc-fqwslf gsqkEc"><div class="sc-cBQMlg kAHhUk"><h2 class="sc-dcKlJK sc-cVttbi gqEuPW ksnHgj">About Dataset</h2></div></div></div><div class="sc-jgvlka jFuPjz"><div class="sc-gzqKSP ktvwwo"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><p>The microscopic blood cell dataset for leukemia detection consists of high-resolution images essential for automated diagnostic systems. Each image captures detailed cellular morphology under standardized conditions, focusing on both normal and abnormal blood cells.</p>
+<hr>
+<p><strong>Key Components:</strong></p>
+<ul>
+<li>Myeloblasts (AML indicators): 12-20 micrometers, round/oval, high nuclear-cytoplasm ratio, visible nucleoli </li>
+<li>Lymphoblasts (ALL indicators): 10-14 micrometers, homogeneous chromatin, minimal cytoplasm</li>
+<li>Normal cells: Mature lymphocytes, neutrophils, monocytes, eosinophils, basophils</li>
+</ul>
+<hr>
+<p><strong>Technical Specifications:</strong><br>
+Resolution: 1024x1024 pixels minimum Staining: Wright-Giemsa Magnification: 100x oil immersion (1000x total) Color: 24-bit RGB Multiple focal planes per sample</p>
+<hr>
+<p><strong>Quality Measures:</strong><br>
+Expert hematopathologist validation Standardized imaging conditions Multiple samples per cell type Detailed preparation documentation Complete technical metadata</p>
+<hr>
+<p><strong>Clinical Applications:</strong><br>
+Normal vs. abnormal cell differentiation Leukemia subtype identification Disease progression monitoring Early detection screening Treatment response assessment</p>
+<hr>
+<p><strong>Image Annotations Include:</strong><br>
+Nuclear patterns and contours Cytoplasmic features Nucleoli presence Cell measurements Abnormal inclusions/Auer rods</p>
+<hr>
+<p><strong>Machine Learning Capabilities:</strong><br>
+Automated cell classification Quantitative feature analysis Differential counting Morphological abnormality detection<br>
+The dataset's structured organization and comprehensive documentation support both research initiatives and clinical applications in blood cancer diagnostics. Its standardized format enables reliable machine learning model development for automated leukemia detection systems.</p>
+<hr>
+<p>This dataset consists of 5000 images (.jpg) where the distribution is 1000 per class</p></div></div></div>
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