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Automatic classification with concatenation of deep and handcrafted features of histological images for breast carcinoma diagnosis

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Abstract

The second leading cause of death from cancer among women is breast cancer. In order to prevent avoidable deaths, early detection is extremely necessary. Malignancy evaluation of tissue biopsies, however, is complicated and based on observer subjectivity. In addition, histological images stained with hematoxylin and eosin (H&E) exhibit a highly variable appearance, also at the same degree of malignancy. In this paper, we propose a classification model based on KNN with the combination of deep and handcrafted features using histological images to diagnose breast cancer. Here, four malignancy levels are considered, namely normal, benign, in situ, and invasive. The classification of four malignancy levels is examined by three classifiers with three sets of deep features and three handcrafted features. The deep features are extracted from the fc6 layer of three pre-trained networks: alexnet, vgg16, and vgg19. The handcrafted features are GLCM, HOG, and LBP. After evaluation, the top-performed classifier, deep feature, and handcrafted features are considered to frame the classification model. The classification model based on fine- KNN with combining the feature of vgg16 and LBP achieved satisfactory diagnostic effectiveness (accuracy) of 84.2% and area under the curve (AUC) of 0.85. Further, the likelihood ratio for positive results (LR+) is greater than 10, i.e., 12.5, which implicates the proposed method has a significant contribution to the diagnosis and a good diagnostic test.

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Authors and Affiliations

  1. Department of Electronics, Sambalpur University, Sambalpur, Odisha, 768019, India

    Prabira Kumar Sethy

  2. Department of Computer Science and Engineering, VSSUT, Burla, 768019, Odisha, India

    Santi Kumari Behera

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  1. Prabira Kumar Sethy
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  2. Santi Kumari Behera
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Correspondence to Prabira Kumar Sethy.

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Sethy, P.K., Behera, S.K. Automatic classification with concatenation of deep and handcrafted features of histological images for breast carcinoma diagnosis. Multimed Tools Appl 81, 9631–9643 (2022). https://doi.org/10.1007/s11042-021-11756-5

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