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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 tNtjD"><div style="min-height: 80px;"><div class="sc-etVRix jqYJaa sc-bMmLMY ZURWJ"><p>Prostate Cancer MRI Classification</p>
+<p>This dataset comprises images related to prostate cancer obtained through Magnetic Resonance Imaging (MRI). Each image is categorized into one of three types based on the MRI sequence used for acquisition: Apparent Diffusion Coefficient (ADC), High B-Value (HBV), and T2-weighted (T2w).</p>
+<ol>
+<li><p>ADC (Apparent Diffusion Coefficient):<br>
+ADC images reflect the diffusion of water molecules within tissues, providing insights into tissue microstructure. Lower ADC values often indicate higher cell density, potentially indicative of cancerous tissue.</p></li>
+<li><p>HBV (High B-Value):<br>
+HBV images are acquired using a sequence with high b-values, enhancing sensitivity to tissue abnormalities. This sequence is particularly useful for detecting areas of restricted diffusion, which can be indicative of cancerous lesions.</p></li>
+<li><p>T2w (T2-weighted):<br>
+T2w images highlight variations in tissue water content, offering excellent contrast between different soft tissues. Abnormalities such as tumors often exhibit distinct characteristics on T2w images, aiding in their detection and characterization.</p></li>
+</ol>
+<p>Utilizing this dataset, researchers and practitioners can develop and train deep learning models for the classification of prostate cancer based on MRI data. By leveraging the unique features captured by each MRI sequence, these models can assist in accurate diagnosis, prognosis, and treatment planning for patients with prostate cancer.</p></div></div></div>
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