PROSTATE CANCER DETECTION USING HISTOPATHOLOGY IMAGES AND CLASSIFICATION USING IMPROVED RideNN

Abstract

Medical industry reports prostate cancer as common and high among men and alarms the necessity for detecting prostate cancer for which the required morphology is extracted from the histopathology images. Commonly, the Gleason grading system remains a perfect factor for grading prostate cancer in men, but pathologists suffer from minute inter- and intra-observer variations. Thus, an automatic method for segmenting and classifying prostate cancer is modeled in this paper. The significance of the developed method is that the segmentation and classification are gland-oriented using the Color Space (CS) transformation and Salp Swarm Optimization Algorithm-based Rider Neural Network (SSA-RideNN). The gland region is considered as the morphology for cancer detection from which the maximal significant regions are extracted as features using multiple-kernel scale-invariant feature transform (MK-SIFT). Here, the RideNN classifier is trained optimally using the proposed Salp–Rider Algorithm (SRA), which is the integration of Salp Swarm Optimization Algorithm (SSA) and Rider Optimization Algorithm (ROA). The experimentation is performed using the histopathology images and the analysis based on sensitivity, accuracy, and specificity reveals that the proposed prostate cancer detection method acquired the maximal accuracy, sensitivity, and specificity of 0.8966, 0.8919, and 0.8596, respectively.

Role of AI and Histopathological Images in Detecting Prostate Cancer: A Survey

“…However, there is no general segmentation technique proven to be effective for all kind of images. In [ 23 ], the segmentation task in prostate cancer is carried out using the color space transformation and thresholding techniques. This process aids to form the gland region, which is subjected to feature extraction by applying multiple-kernel scale-invariant feature transform method.…”

“…Aside from CNN, many authors have tried to utilize different techniques in histopathology imagery in prostate cancer, for example, the authors in [ 23 ] proposed a new deep learning technique that combines the multi-model neural network, ride NN and optimization algorithm, Salp–Rider algorithm (SRA), generating the new technique SSA-RideNN. The experiments showed that SSA-RideNN attained a maximal accuracy, specificity, and sensitivity.…”

“…In the last decade, researchers have turned their focus towards the development of new deep learning techniques as they outperform conventional machine learning techniques in diverse fields and not only HI image analysis. To date, many of these ML techniques have been supplanted by DL, and an abundance of work has evaluated the use of deep learning techniques on HI of prostate cancer [ 23 , 24 , 25 , 26 , 27 , 28 , 29 , 30 , 31 , 32 , 33 ]. Moreover, studies that employ an ensemble of DL techniques and ML techniques gave better results [ 34 ].…”

Wearable IoT based diagnosis of prostate cancer using GLCM-multiclass SVM and SIFT-multiclass SVM feature extraction strategies

“…A total of 13 characteristics were calculated, the subset of which provided over 87% of accuracy in grading of the prostate. Gurav et al (2019) have made an attempt to automate the Gleason grading method to detect the stage of the prostate cancer using the color space transformation method in combination with rider neural network. Here, SIFT approach was used to extract the features from the histopathological images.…”