###### Abstract
They split the data into two sets: 75% for training and 25% for testing. Their models were built using the training set, and the testing set was used to evaluate their models performance. The researchers found that ensemble algorithms performed better than the other methods on the entire gene list. Ensemble algorithms are a techniques or algorithm that combines the predictions of multiple models. This can help to enhance the models' performance by reducing the bias and variance of individual models. At the same time, the clustering and classification models achieved higher performance when features (genes) were reduced to 20 genes. [PERSON] et al. [18] created a multitask model based on deep learning for classifying tissue, disease condition, tissue origin, and neoplastic subtype using the full transcriptome (RNA-seq) datasets peri-neoplastic, neoplastic, and non-neoplastic tissue. Their results indicated that the model achieved 99% accuracy for classifying disease states, an accuracy of 97% for classifying tissue origin, and an accuracy of 92% for subclassification of neoplastic. [PERSON] and [PERSON] [19] proposed a gene transformer deep learning-based model to detect the significant biomarkers across different cancer subtypes. They used gene expression data of 33 tumor types from the TCGA. Their results indicated that their proposed model outperformed the traditional classification models. [PERSON] et al. [20] developed an explainable deep learning model called Transformer for Gene Expression Modeling (T-GEM) for predicting the cancer types and identifying the type of immune cell using TCGA and ScRNA-Seq data, respectively. Moreover, they used their proposed model to obtain the relevant markers. Their results depicted that their developed model has accuracies of 94.92% and 90.73% for the TCGA and PBMC ScRNA-Seq datasets, respectively. [PERSON] et al. [21] created a transformer deep learning model called DeePathNet that combines omics data and pathways information. They used the datasetsTCGA, CCLE, and ProCan-DepMapSanger. The performance of their proposed model was assessed by classifying cancer types and subtypes, in addition to the prediction of the drug response. Their proposed model outperformed the traditional classifiers by achieving over 95% recall scores for most cancer types. In this paper, we constructed a CNNs model that classifies 33 cancer types and normal samples using RNA-Seq gene expressions data as inputs. The Illumina HiSeq platform and R software are used to obtain gene expression data from Pan-Cancer Atlas [22] via the RTCGAToolbox package [23,24]. We selected the state run data of 28-01-2016, which is determined using the getFirehoseRunningDates function. Consequently, the getFirehoseData function is used to download the gene expression data. Then, we processed the downloaded data using a normalization technique to the data to ensure that the expression could be inferred properly from the gene expression data and prevent the occurrence of biased expression scores. The normalized data is processed using filtration through the gene-filter package to filter the genes that exhibit low variation across the samples.