Automatic detection and classification of lung cancer CT scans based on deep learning and ebola optimization search algorithm

Abstract

Recently, research has shown an increased spread of non-communicable diseases such as cancer. Lung cancer diagnosis and detection has become one of the biggest obstacles in recent years. Early lung cancer diagnosis and detection would reliably promote safety and the survival of many lives globally. The precise classification of lung cancer using medical images will help physicians select suitable therapy to reduce cancer mortality. Much work has been carried out in lung cancer detection using CNN. However, lung cancer prediction still becomes difficult due to the multifaceted designs in the CT scan. Moreover, CNN models have challenges that affect their performance, including choosing the optimal architecture, selecting suitable model parameters, and picking the best values for weights and biases. To address the problem of selecting optimal weight and bias combination required for classification of lung cancer in CT images, this study proposes a hybrid metaheuristic and CNN algorithm. We first designed a CNN architecture and then computed the solution vector of the model. The resulting solution vector was passed to the Ebola optimization search algorithm (EOSA) to select the best combination of weights and bias to train the CNN model to handle the classification problem. After thoroughly training the EOSA-CNN hybrid model, we obtained the optimal configuration, which yielded good performance. Experimentation with the publicly accessible Iraq-Oncology Teaching Hospital / National Center for Cancer Diseases (IQ-OTH/NCCD) lung cancer dataset showed that the EOSA metaheuristic algorithm yielded a classification accuracy of 0.9321. Similarly, the performance comparisons of EOSA-CNN with other methods, namely, GA-CNN, LCBO-CNN, MVO-CNN, SBO-CNN, WOA-CNN, and the classical CNN, were also computed and presented. The result showed that EOSA-CNN achieved a specificity of 0.7941, 0.97951, 0.9328, and sensitivity of 0.9038, 0.13333, and 0.9071 for normal, benign, and malignant cases, respectively. This confirms that the hybrid algorithm provides a good solution for the classification of lung cancer.

Colon and lung cancer classification from multi-modal images using resilient and efficient neural network architectures

“… Both the D1 and D2 models were trained on 80 % of the IQ-OTHNCCD dataset (878 images) and achieved accuracies of 92.24 % and 91.78 %, respectively, with an ensemble performance of 93 %. These results surpassed recent performances reported by Chen et al [ 51 ] and Bangare et al [ 50 ] and were on par with performances reported by Mohamed et al [ 49 ] and Al-Yasriy et al [ 56 ]. …”

“… Scenario Feature Extraction (ImageNet Pre-trained) Epoch Test Accuracy (%) Benchmark Accuracy (%) Comments 1 DenseNet201 05 99.96 98.50 (2023) [ 7 ], 99.30 (2023) [ 12 ], 98.96 (2023) [ 13 ] D1 performs better than the benchmarks, proving its superiority. 2 55 92.24 93.21 (2023) [ 49 ], 86.42 (2022) [ 50 ], 88.00 (2021) [ 51 ] Both D1 and D2 significantly outperform Chen et al [ 51 ] and Bangare et al [ 50 ], while the ensemble performs parallelly to Mohamed et al [ 49 ]. This proves the multi-modality and robustness of D1 and D2.…”

A New Hybrid Adaptive Optimization Algorithm Based Wavelet Neural Network for Severity Level Prediction for Lung Cancer Dataset

“…TP TP+FP (23) Recall: It precisely quantifies the proportion of recognized risk factors for lung cancer out of the total quantity. The sensitivity or recall is computed using Eq.…”

An optimized convolutional neural network architecture for lung cancer detection

“…In Ref. 8 , Mohamed et al proposed a CNN and hybrid metaheuristic approach for lung cancer classification. Initially, a CNN architecture was designed, and its solution vector was computed.…”