A Novel Deep Learning-based Whale Optimization Algorithm for Prediction of Breast Cancer

HIGHLIGHTS Novel whale optimization algorithm is proposed for prediction of breast cancer. Deep learning-based WOA adjusts the CNN structure as per maximum detection accuracy. Proposed method achieves 92.4% accuracy in comparison to 90.3%. Validity of method is evaluated with magnifying factors like 40x, 100 x, 200x, 400x.

Abstract Breast cancer is one of the most common cancers among women that cause billions of deaths worldwide. Identification of breast cancer often depends on the examination of digital biomedical photography such as the histopathological images of various health professionals, and clinicians. Analyzing histopathological images is a unique task and always requires special knowledge to conclude investigating these types of images. In this paper, a novel efficient technique has been proposed for the detection and prediction of breast cancer at its early stage. Initially, the dataset of images is used to carry out the pre-processing phase, which helps to transform a human pictorial image into a computer photographic image and adjust the parameters appropriate to the Convolutional neural network (CNN) classifier. Afterward, all the transformed images are assigned to the CNN classifier for the training process. CNN classifies incoming breast cancer clinical images as malignant and benign without prior information about the occurrence of cancer. For parameter optimization of CNN, a deep learning-based whale optimization algorithm (WOA) has been proposed which proficiently and automatically adjusts the CNN network structure by maximizing the detection accuracy. We have also compared the obtained accuracy of the proposed algorithm with a standard CNN and other existing classifiers and it is found that the proposed algorithm supersedes the other existing algorithms.

Study of clinical profile of patients of bipolar mood disorder with and without substance abuse and clinical course variables in the substance abusing bipolar group in one of the tertiary care centre of North India

Background: Bipolar disorder is the sixth leading cause of disability worldwide and has a lifetime prevalence of about 3% in general population. In patients with bipolar disorder there was 58 % lifetime prevalence of co-occurring alcohol abuse and a 38 % lifetime prevalence of co-occurring other substance abuse. Substance abuse interferes with treatment and management approaches of the bipolar disorder.Methods: A cross sectional observational study of 120 male patients divided in substance abusing (60) and non-substance abusing groups (60) with bipolar disorder according to DSM-V, who met the Inclusion criteria . A written informed consent was obtained from the patients and/ or their family members. Patient’s information was recorded on the socio-demographic and clinical profile sheet .Thereafter, YMRS or HAM-D scales were applied as per the phase of the illness.Results: Most of the patients were between 15-25 years in SAB group and 35-50 years in NSAB group, educated, semiskilled and married. Tobacco abuse was the commonest followed by cannabis and alcohol abuse. The mean duration of hospital stay in SAB group was 41.40 days and in NSAB group was 43.20 days. Dysphoric mania, aggressive behavior and suicidal attempts were more in SAB group. Mean total YMRS score of SAB group was greater than NSAB group.Conclusions: Maximum patients had onset of substance abuse before the onset of affective symptoms. Manic symptomatology was more severe in substance abusing group.

Inter-hospital transfer of referred trauma patients admitted to the trauma centre of King George Medical University

Background: Inter-hospital transfer is a common in trauma victims due to paucity of super-specialty care, lack of specialty beds, and lack of funding. The government of Uttar Pradesh introduced Emergency Medical Response system (EMRS). There is a need to audit and evaluate the transfer process since the introduction of the service. The objectives of this study were to identify critical gaps in the transfer of trauma patients and secondary over triage to the trauma centre of KGMU.Methods: This prospective observational study was conducted on trauma victims referred to the trauma centre. Patients were evaluated for clinical status, Injury Severity Score, protective patient devices present, type of intravenous fluid infusion, mode of transport, and level of referring hospital. Transfer records, transport vehicles and accompanying personal were evaluated.Results: Of the 342 patients enrolled in the study, 91 had a GCS score <8 and 112(32.74%) had a diastolic BP <60 mm Hg at arrival. Twenty patients (5.8%) were referred from tertiary care centers, 74(21.6%) were referred from district hospitals, 136(39.76%) were referred from primary care centers and 112(32.74%) were referred from private hospitals. Date and time of injury was not recorded in any of the patients. Referral time was recorded in 48(14.03%) patients. One hundred seventy-six patients (51.14%) were transferred in EMRS ambulances, 102(29.82%) patients met the criteria for secondary over triage.Conclusions: There is a need to adopt and strictly implement guidelines for transfer of trauma victims to plug the critical gaps in the transfer process.