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Pradhan, Manaswini
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Lung cancer segmentation and classification using hybrid CNN-LSTM model Pradhan, Manaswini; Alkhayyat, Ahmed
Indonesian Journal of Electrical Engineering and Computer Science Vol 41, No 1: January 2026
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijeecs.v41.i1.pp309-319

Abstract

A collection of genetic disorders and various types of abnormalities in the metabolism lead to cancer, a fatal disease. Lung and colon cancer are found to be main causes of death and infirmity in people. When choosing the best course of treatment, the diagnosis of these tumors is usually the most important consideration. This study's main objectives are to classify lung cancer and its severity, as well as to recognize malignant lung nodules. The suggested approach additionally classifies the stages of lung cancer in order to recognize lung nodules. The convolutional neural network (CNN) is used to detect lung nodules, identifying a nodule which is accurately segmented and classified. The suggested method is separated into dual parts: primarily, it classifies normal and abnormal behavior, and the subsequent one classifies the different stages of lung cancer. Texture and intensity-based features are extracted during the classification stage. When compared to other methods such as nested long short-term memory (LSTM)+ CNN, the hybrid CNN LSTM obtains superior outcomes in terms of accuracy (99.35%), specificity (99.30%), sensitivity (99.32%), and F1-score (99.29%).
Co-Authors Alkhayyat, Ahmed