![]() No model or method in the literature could ever classify such a diversified mix of tumor images with such high accuracy. The training and validation accuracy along with the ROC-AUC scores have been found satisfactory over the existing models. Results have been compared with standard sequential deep learning models and notable recent studies. 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. The proposed model has been used for classifying 717 subjects comprising different imaging modalities and varied Tumor-Node-Metastasis 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. ![]() Thus, there is ample opportunity to study if a heterogeneous collection of tumor images can be classified according to their respective stages. This has enriched the domain-specific knowledge of malignant tumor prediction, we are devoid of an efficient model that may predict the stages of tumors irrespective of their origin. ![]() Most of the studies have considered a particular tumor genre categorized according to its originating organ. Many significant efforts have so far been made to classify malignant tumors by using various machine learning methods. ![]()
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