This project is about the detection of lung cancer by training a model of deep neural networks using histopathological lung cancer tissue images. Deferent models have been proposed for detecting lung cancer cells automatically involving Inception V3, Random Forest, and convolutional neural network (CNN). The deep convolutional neural network has been trained to extract important features that facilitate build detection and diagnosis of lung cancer cells more efficiently and accurately. The proposed method in this project has accomplished promising and satisfactory results in terms of accuracy, precision, recall, F-score, and specificity measure in lung cancer detection. Furthermore, it has been applied on dataset which contains 178,000 photos. The accuracy values that are obtained are accuracy 97.09%, precision 96.89%, recall 97.31%, F-score measure 97.09%, and specificity measure 96.88%.

The karyotyping technique is importance in the diagnosis of genetic diseases of humans, including the diagnosis the genetic disorders of prenatal and cancer. In order to obtain the karyotype system for the metaphase images of chromosomes using the image processing. Assembling of human chromosomes pairs from the metaphase image will pass in several stages which are segmentation (single chromosome, overlapped and touching chromosome), feature extraction, matching and classification. In this study has been focus on the separation of touching and overlapped. The problem of separation touching and overlapped chromosome was solved using the convulsion mask and with a help the morphological thinning and contour. The separation of touching and overlapped chromosome is according on finding cut points. The thinning of the image is obtained which helps to identification the Region of Interest. The contour of the image is obtained which helps to explain the entire shape of the image and find out the cut points for separation between chromosomes. In this study an efficient algorithms was used to separation the cluster of touching and overlapping chromosomes. The algorithm of separation touching and overlapping was implemented by using sequential stages (initially, segmentation from metaphase image, convert to the binary image, extract thinning, using mask with 9*9, extract contour, using mask 7*7, identified the cut points and finally, separate touching or overlapping chromosome). This algorithm capable to isolate a cluster of touching chromosomes and a cluster of touching and overlapping chromosomes.