Feature Selection Based on Dialectical Optimization Algorithm for Breast Lesion Classification in Thermographic Images

Pereira, Jessiane Mônica Silva; Santana, Maíra Araújo de; Lima, Clarisse Lins de; Lima, Ricardo Carvalho; Lima, Sidney Marlon Lopes de; Santos, Wellington Pinheiro dos

doi:10.4018/978-1-6684-7136-4.ch005

Cited by 3 publications

(1 citation statement)

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“…Recently, Atban et al [ 22 ] presented an optimized deep learning approach for improved breast cancer classification. Peirera et al [ 23 ] presented a dialectical feature selection technique for enhanced breast cancer classification; however, these techniques face the problem of termination where the optimal values have been retrieved.…”

Section: Introductionmentioning

confidence: 99%

BC2NetRF: Breast Cancer Classification from Mammogram Images Using Enhanced Deep Learning Features and Equilibrium-Jaya Controlled Regula Falsi-Based Features Selection

et al. 2023

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One of the most frequent cancers in women is breast cancer, and in the year 2022, approximately 287,850 new cases have been diagnosed. From them, 43,250 women died from this cancer. An early diagnosis of this cancer can help to overcome the mortality rate. However, the manual diagnosis of this cancer using mammogram images is not an easy process and always requires an expert person. Several AI-based techniques have been suggested in the literature. However, still, they are facing several challenges, such as similarities between cancer and non-cancer regions, irrelevant feature extraction, and weak training models. In this work, we proposed a new automated computerized framework for breast cancer classification. The proposed framework improves the contrast using a novel enhancement technique called haze-reduced local-global. The enhanced images are later employed for the dataset augmentation. This step aimed at increasing the diversity of the dataset and improving the training capability of the selected deep learning model. After that, a pre-trained model named EfficientNet-b0 was employed and fine-tuned to add a few new layers. The fine-tuned model was trained separately on original and enhanced images using deep transfer learning concepts with static hyperparameters’ initialization. Deep features were extracted from the average pooling layer in the next step and fused using a new serial-based approach. The fused features were later optimized using a feature selection algorithm known as Equilibrium-Jaya controlled Regula Falsi. The Regula Falsi was employed as a termination function in this algorithm. The selected features were finally classified using several machine learning classifiers. The experimental process was conducted on two publicly available datasets—CBIS-DDSM and INbreast. For these datasets, the achieved average accuracy is 95.4% and 99.7%. A comparison with state-of-the-art (SOTA) technology shows that the obtained proposed framework improved the accuracy. Moreover, the confidence interval-based analysis shows consistent results of the proposed framework.

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Section: Introductionmentioning

confidence: 99%