SDCT-AuxNet : DCT augmented stain deconvolutional CNN with auxiliary classifier for cancer diagnosis

Abstract

Acute lymphoblastic leukemia (ALL) is a pervasive pediatric white blood cell cancer across the globe. With the popularity of convolutional neural networks (CNNs), computer-aided diagnosis of cancer has attracted considerable attention. Such tools are easily deployable and are cost-effective. Hence, these can enable extensive coverage of cancer diagnostic facilities. However, the development of such a tool for ALL cancer was challenging so far due to the non-availability of a large training dataset. The visual similarity between the malignant and normal cells adds to the complexity of the problem. This paper discusses the recent release of a large dataset and presents a novel deep learning architecture for the classification of cell images of ALL cancer. The proposed architecture, namely, SDCT-AuxNet θ is a 2-module framework that utilizes a compact CNN as the main classifier in one module and a Kernel SVM as the auxiliary classifier in the other one. While CNN classifier uses features through bilinear-pooling, spectral-averaged features are used by the auxiliary classifier. Further, this CNN is trained on the stain deconvolved quantity images in the optical density domain instead of the conventional RGB images. A novel test strategy is proposed that exploits both the classifiers for decision making using the confidence scores of their predicted class labels. Elaborate experiments have been carried out on our recently released public dataset of 15114 images of ALL cancer and healthy cells to establish the validity of the proposed methodology that is also robust to subject-level variability. A weighted F1 score of 94.8% is obtained that is best so far on this challenging dataset.

Automated Detection Model in Classification B-Lymphoblast Cell from Normal B-Lymphoid Precursors in Blood Smear Microscopic Images Based on the Majority Voting Technique

“…Herein, we introduce a novel nucleus detection method based on a CNN pre-trained model in which, by using four state-of-the-art CNNs, a model is proposed for the classification of B-ALL from normal lymphocytes. The C-NMC dataset [11][12][13][14] comprised 12528 lymphocyte nucleus images, of which 8491 belonged to B-ALL lymphoblast and 4037 to normal B-lymphoid cases. The dataset cell nuclei were segmented from the microscopic images in the real world because these contain some staining noise and illumination error, although an expert via an in-house method of stain color normalization has largely fixed these errors.…”

Identifying Malignant Lymph Nodes of Prostate Cancer Patients Using A Combination of Pre-Trained Deep Models and Traditional Machine Learning Classifiers

“…[10] and [11] implemented CNNs and their variants in automatic lesion detection, and multiple abnormality detection from medical images. [12] developed a deconvolutional CNN for classi cation of acute lymphoblastic leukemia, a type of cancer of the white blood cell. [13] designed a multi-network feature extraction model using pre-trained deep CNNs to aid the breast cancer diagnosis.…”

Generalized SIR (GSIR) epidemic model: An improved framework for the predictive monitoring of COVID-19 pandemic

“…Hence, the trend and variability analysis of the data using the discrete cosine transform (DCT) based Fourier decomposition method (FDM) [20] , [21] followed by Gaussian mixture model (GMM) for COVID-19 prediction has been used recently in [22] . The FDM is based on DCT that works as an optimal transform for the first order Gauss–Markov random signals and has been proved useful in various applications [23] , [24] , [25] , [26] . Authors in [27] presented predictions related to the spread of COVID-19 disease in Italy, France, and China.…”