# Analyzing RNA-Seq Gene Expression Data Using Deep Learning Approaches for Cancer Classification
## 5 Conclusions  
Early prognosis and treatment of cancer are highly significant, as mortality due cancer remains very high. RNA-Seq data for multiple cancer types is beneficial in this regard, as it assists in identifying differentially expressed genes and the relationship among different genes that occur in our bodies. However, this is a complex process, as relatively few samples are available, \begin{table}
\begin{tabular}{c c c c c c} \hline \hline  & **Dataset** & \
\begin{tabular}{c} **Number of** \\ **Samples** \\ \end{tabular} & **Optimization** & **Preprocessing** &
\begin{tabular}{c} **Accuracy for** \\ **5 Classes** \\ \end{tabular} \\ \hline
[22] & \begin{tabular}{c} TCGA \\ Dataset \\ \end{tabular} & \
\begin{tabular}{c} samples \(\times\) \\ genes \\ \end{tabular} & No &
\begin{tabular}{c} Y = log2 (x + 1) \\ variance threshold \\ 102 \(\times\) 102 image \\ \end{tabular} & 95.59\% \\ \hline
[27] & \begin{tabular}{c} Mendeley \\ Dataset \\ \end{tabular} & \begin{tabular}{c} 2086 samples \\ \(\times\) 971 genes \\ \end{tabular} & BPSO-DT & \
\begin{tabular}{c} BPSO-DT \\ \end{tabular} &
\begin{tabular}{c} Equation (1) \\ \end{tabular} & 96.90\% \\ \cline{3-6}  & & & & No threshold \\ \cline{3-6}  & & & & 25\(\times\)25 image \\ \hline
[16] & \begin{tabular}{c} TCGA \\ Dataset \\ \end{tabular} & \begin{tabular}{c} samples \(\times\) \\ genes \\ \end{tabular} & No & \begin{tabular}{c} Y = log2 (x + 1) \\ variance threshold \\ 102 \(\times\) 102 image \\ \end{tabular} & 95.65\% \\ \hline Proposed & \begin{tabular}{c} Mendeley \\ Dataset \\ \end{tabular} & \
\begin{tabular}{c} 2086 samples \\ \(\times\) 971 genes \\ \end{tabular} & No &
\begin{tabular}{c} Equation (1) \\ \end{tabular} & 97\% \\ \cline{3-6}  & & & & 32 \(\times\) 32 image \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Comparison of results of DL models with related work in gene expression data. Figure 4: Precision, Recall, F1 –Score, and Accuracy for CNN with augmentation. Figure 5: Heatmap for CNN for 70–30 split with augmentation. and those that are available are high dimensional and have missing and duplicated values. Therefore, in this study, a novel approach has been proposed that tackles high dimensional and noisy RNA-Seq data. The RNA-Seq data used in this study was extracted from the Mendeley repository developed by Elsevier, a publishing company at Netherlands. The dataset contains information on five types of cancer, i.e., kidney renal clear cell carcinoma (KIRC), BRCA, lung squamous cell carcinoma (LUSC), lung adenocarcinoma(LUAD), and uterine corpus endometrial carcinoma(UCEC). In the first step, preprocessing is done, in which 1D data are converted to 2D images of size 32 \(\times\) 2. In the next step, relevant features are extracted and selected using DL algorithms. As the last step, a classification task is performed through eight deep Learning based algorithms (CNN, ResNet152, ResNet50, ResNet152, GoogleNet, AlexNet, VGG16, and VGG19). Deep learning models have shown promising results; in this study, the highest obtained highest was 97%. CNN outperformed all other DL algorithms. In this paper, we made classification of five cancers based on the related genes identified by the DL algorithms. Comparative analysis with state of the art work is also conducted and the results achieved by proposed work outperformed the work presented in literature for the same cancer types. For future prospects, the proposed methodology can be used to identify the best accurate method for identification of cancer related biomarkers for each cancer type. Functional pathway analysis of related genes for these cancer types can also be conducted with the help of [PERSON] website [61] or IntPath website [62]. Deep Learning architectures can be implemented to identify promising features from 2D images and these features could be incorporated to identify cancer specific biomarkers. Formal analysis, D.B.R. ; Investigation, W.H.B. ; Methodology, K.N. ; Resources, M.S.A.K. ; Writing--review & editing, L.R. and A.A.A.I.