Comparison of Multimodal Tumor Image Segmentation Techniques

Moitra, Dipanjan

doi:10.26483/ijarcs.v9i3.6010

Cited by 7 publications

(1 citation statement)

References 3 publications

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“…DNN has a wide variety of applications ranging from the estimation of blood pressure [52,70] to the detection of COVID-19 infections [1,2]. Unlike the traditional machine learning algorithms, explicit image pre-processing [37], segmentation [38], and manual feature crafting are not required in deep learning. Thus, image processing and re-generation becomes easier and effective with deep learning techniques [44].…”

Section: Introductionmentioning

confidence: 99%

Classification of malignant tumors by a non-sequential recurrent ensemble of deep neural network model

Moitra

Mandal

2022

Multimed Tools Appl

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Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. Most of the studies have considered a particular tumor genre categorized according to its originating organ. This has enriched the domainspecific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. The present research work has prepared a heterogeneous tumor dataset comprising eight different datasets from The Cancer Imaging Archives and classified them according to their respective stages, as suggested by the American Joint Committee on Cancer. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis stages. A new non-sequential deep hybrid model ensemble has been developed by exploiting branched and re-injected layers, followed by bidirectional recurrent layers to classify tumor images. Results have been compared with standard sequential deep learning models and notable recent studies. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The proposed model may help radiologists by acting as an auxiliary decision support system and speed up the tumor diagnosis process.

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