Lung Cancer Segmentation

Le résumé fourni par la source

Lung cancer region segmentation through medical image information serves as an essential element of computer-aided diagnosis systems. We present a technique in this paper that uses the Vector of Support Machine (SVM) technique to achieve this. The feature vectors from the extracted attributes are found in two datasets that are used to train the SVM. Post-processing techniques are applied to enhance the segmentation results after the testing set's regions are classified using the trained supported vector machine (SVM). The dice coefficient, which is calculated F1 score, precision, and recall are used to evaluate the algorithm's performance. The proposed SVM-based algorithm is an important tool for lung disease segmentation and advances the field of computer-aided diagnosis and medical image analysis.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.