Bulletin of Electrical Engineering and Informatics Vol. 13, No. 1, February 2024, pp. 510~518 ISSN: 2302-9285, DOI: 10.11591/eei.v13i1.6469  510 Journal homepage: http://beei.org A deep learning-based intelligent decision-making model for tumor and cancer cell identification Putta Durga, Deepthi Godavarthi School of Computer Science and Engineering, VIT-AP University, Amaravati, India Article Info ABSTRACT Article history: Received Apr 12, 2023 Revised Jun 4, 2023 Accepted Aug 2, 2023 In the current era, the prevalence of common ailments is leading to an increasing number of fatalities. Various infections, viruses, and other pathogens can cause these illnesses. Some illnesses can give rise to tumors that seriously threaten human health. Distinct forms of tumors exist, including benign, premalignant, and malignant, with cancer being present only in malignant forms. Deep learning (DL) algorithms have emerged as one of the most promising methods for detecting cancers within the human body. However, existing models face criticism for their limitations, such as lack of support for large datasets, and reliance on a limited number of attributes from input images. To address these limitations and enable efficient cancer detection throughout the human body, an intelligent decision-making approach model (IDMA) is proposed. The IDMA is combined with the pre-trained VGG19 for improved training. The IDMA analyses convolutional neural network (CNN) layer images for signs of malignancy and rules out false positives. Various performance indicators, like sensitivity, precision, recall, and F1-score, are used to assess the system's performance. The suggested system has been evaluated and proven to outperform similar current systems, achieving an impressive 98.67% accuracy in detecting cancer cells. Keywords: Cancer cells Deep learning Intelligent decision-making approach Tumors VGG-19 This is an open access article under the CC BY-SA license. Corresponding Author: Deepthi Godavarthi School of Computer Science and Engineering, VIT-AP University Amaravati, Andhra Pradesh, India Email: deepthi.g@vitap.ac.in 1. INTRODUCTION Deep learning (DL) plays a crucial role in healthcare systems, especially in the area of disease prediction [1]. As tumors evolve, it becomes harder for current algorithms and models to keep up. In the brain, tumors come in 120 different varieties. There are both rapidly developing and slowly growing cancers. Rapidly expanding tumors, which can progress into cancer, pose the greatest risk to people of all ages. Experts have a hard time using automation to determine where a tumor is in its progression [2]. As time goes on, non-communicable diseases (NCDs) become the leading cause of death worldwide. For a long time now, scientists have been working on AI-based systems that use decision-making strategies to lessen the burden of disease prediction [3]. Disease prediction problems can also be tackled with the use of computer-based clinical decision support systems (CDSSs) [4]. The CDSS can be used to classify the progression of cancer in a wide range of tumor types. Several different types of automated methods, such as content-based image retrieval (CBIR), have been developed to identify brain cancers. The primary focus of this method is on comparing the outcomes of both low and higher-level visual data extraction from magnetic resonance imaging (MRI) scans [5]. These tiers are utilized in order to lessen the space. Disease prediction using IoT devices at the edge is a common