From last decade, lung cancer become sign of fear among the people all over the world. As a result, many countries generate funds and give invitation to many scholars to overcome on this disease. Many researchers proposed many solutions and challenges of different phases of computer aided system to detect the lung cancer in early stages and give the facts about the lung cancer. CV (Computer Vision) play vital role to prevent lung cancer. Since image processing is necessary for computer vision, further in medical image processing there are many technical steps which are necessary to improve the performance of medical diagnostic machines. Without such steps programmer is unable to achieve accuracy given by another author using specific algorithm or technique. In this paper we highlight such steps which are used by many author in pre-processing, segmentation and classification methods of lung cancer area detection. If pre-processing and segmentation process have some ambiguity than ultimately it effects on classification process. We discuss such factors briefly so that new researchers can easily understand the situation to work further in which direction.

Glioblastoma (GBM), a very aggressive type of brain tumor, sometimes creates a chemoresistant state that compromises the effectiveness of chemotherapy and leads to serious clinical complications in treatment. Predicting drug resistance is crucial for the improvement of medication effect during cancer treatment. Assessing drug resistance is difficult due to the pricey chemotherapeutic trails and prolonged laboratory investigations. Deep learning plays a significant role in drug resistance prediction nowadays. This paper presents a novel deep learning model that combines Convolutional Neural Networks (CNN), Long Short Term Memory Networks (LSTM), and transformer architectures to predict drug resistance. The proposed application acts as a system that estimate the resistance of drugs based on gene expression details and chemical properties. As compared with existing model for drug resistance prediction, proposed model achieved lower Mean Squared Error (MSE) of 0.4109 and Mean Absolute Error (MAE) of 0.5040, along with higher R² of 0.9635 and pearson correlation of 0.9999. This work significantly advances the fields of pharmacogenomics and personalized medicine through an in-depth evaluation that includes complex performance metrics and visualizations.

Glioblastoma (GBM), a common cancer of the central nervous system (CNS), is considered incurable worldwide. The treatment of GBM varies from patient to patient, as conventional medical treatments do not apply to all patients with similar symptoms. Therefore, drug efficacy recommendation systems are very useful in the treatment of various types of cancers. The Genomics of Drug Sensitivity in Cancer (GDSC) database was used as the primary data source, containing 135,242 cell perturbations, inhibitory concentration of cancer drug for more then 800 cancer cell lines. Each cell line provides gene expression values, which are further normalized through Z-transformation for each gene. However, we utilized only 47 genes in our research due to limitations in computer processing speed and memory. A drug efficacy recommendation system was constructed using a deep learning method that combines gene expression, drug Simplified Molecular Input Line Entry System (SMILES), and inhibitory concentration features. A panel of 47 genes associated with GBM, including BCL6, MAFB, MAP2K4, NONO, S100A7, GNAS, CACNA1D, LMO1, SMAD3, SRC, TRRAP, NCOA1, CDC73, BAZ1A, ABL1, CAMTA1, GATA3, POT1, NFIB, ERBB2, MDM2, CDK4, PTEN, TP53, DGCR8, CTNND1, PRCC, PML, MUC1, PTPN11, H3F3B, NUP214, MYCN, TRIM33, KRAS, RHOA, PBX1, PDGFR, NF1, PIK3CA, EGFR, IDH1, RB1, ATP1A1, SMARCB1, ID3, and IL7R, was processed using two deep learning models: Artificial Neural Network (ANN) and Convolutional Neural Network (CNN). This approach addresses the challenge of personalized treatment for GBM, offering the potential for improved therapeutic outcomes. The results of the recommendation system are calculated based on Half Maximal Inhibitory Concentration (IC50) values, which represent the therapeutic effectiveness in inhibiting the growth of GBM cells. CNN outperformed ANN with a significant margin, achieving a Root Mean Square Error (RMSE) of 0.9822 compared to 1.2127. These results are also consistent with other metrics, including Pearson correlation, Spearman correlation, and Mean Absolute Error (MAE). According to the study, the system can accurately predict the effectiveness of drugs on GBM cancer genes. This study has the potential to predict drug efficacy during medical procedures.