Rapid Development of Achalasia After SARS-CoV-2 Infection: Polymerase Chain Reaction Analysis of Esophageal Muscle Tissue.

INTRODUCTION: Achalasia has been linked to viruses. We have observed cases of rapid-developing achalasia post-coronavirus disease 2019 (COVID-19). METHODS: We aimed to prospectively evaluate esophageal muscle for severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2) from patients with rapid-onset achalasia post-COVID-19 and compare them with achalasia predating COVID-19 and achalasia with no COVID-19. RESULTS: Compared with long-standing achalasia predating COVID-19 and long-standing achalasia with no COVID-19, the subjects with achalasia post-COVID-19 had significantly higher levels of messenger RNA for the SARS-CoV-2 nucleocapsid (N) protein, which correlated with a significant increase in the inflammatory markers NOD-like receptor family pyrin domain-containing 3 and tumor necrosis factor. DISCUSSION: SARS-CoV-2, the virus responsible for COVID-19, is a possible trigger for achalasia.

Maximizing biodiesel yield of a non-edible chinaberry seed oil via microwave assisted transesterification process using response surface methodology and artificial neural network techniques.

In this study, the non-edible Chinaberry Seed Oil (CBO) is converted into biodiesel using microwave assisted transesterification. The objective of this effort is to maximize the biodiesel yield by optimizing the operating parameters, such as catalyst concentration, methanol-oil ratio, reaction speed, and reaction time. The designed setup provides a controlled and effective approach for turning CBO into biodiesel, resulting in encouraging yields and reduced reaction times. The experimental findings reveal the optimal parameters for the highest biodiesel yield (95 %) are a catalyst concentration of 1.5 w/w, a methanol-oil ratio of 6:1 v/v, a reaction speed of 400 RPM, and a reaction period of 3 min. The interaction of the several operating parameters on biodiesel yield has been investigated using two methodologies: Response Surface Methodology (RSM) and Artificial Neural Network (ANN). RSM provides better modeling of parameter interaction, while ANN exhibits lower comparative error when predicting biodiesel yield based on the reaction parameters. The percentage improvement in prediction of biodiesel yield by ANN is found to be 12 % as compared to RSM. This study emphasizes the merits of both the approaches for biodiesel yield optimization. Furthermore, the scaling up this microwave-assisted transesterification system for industrial biodiesel production has been proposes with focus on its economic viability and environmental effects.

Reduction in Arterial Stiffness Index (SI) in Response to Combination Antioxidant Therapy.

Antioxidants reduce arterial stiffness, but the effects previously reported are weak. A systematic review of the antioxidants vitamin E, vitamin C, vitamin A, and beta-carotenes (the most commonly studied antioxidants) on pulse wave velocity (PWV) found an effect size of only -0.20 (approximately -16 m/s or -2.5%). Studies in rats of the potent pro-oxidant substance acetaldehyde have shown that combinations of sulfur-containing antioxidants, including thiamine and l-cysteine, with ascorbic acid potently protect against oxidative-stress-mediated mortality. The effects of these combinations of oxidants on PWV have not been studied. The present study evaluated the effects of 2 weeks of therapy with a combination of sulfur-containing antioxidants (cysteine, thiamine, and pyridoxine) in combination with ascorbic acid on stiffness index (SI), a measure of arterial stiffness that is strongly correlated with PWV, using a Pulse Trace recorder in a diverse group of 78 volunteers. SI fell by -1.7 m/s relative to placebo (95% confidence intervals -0.6 to -2.7 m/s), a reduction of -19% (95% confidence intervals -9% to -31%). The Glass effect size was 1.4, indicating a very strong treatment effect which was substantially greater than the effect size found in previous studies of antioxidants. PWV reduction was correlated significantly with increasing age. Further studies of similar antioxidant combinations are required to determine whether they are of value in the treatment or prevention of cardiovascular disease.

Persistent Apoptotic Enteropathy in an Individual With Marginal Cell Lymphoma.

Apoptotic enteropathy is a histological finding of increased crypt apoptosis that is commonly associated with diarrhea. Several etiologies for this disease state including immunodeficiency, autoimmune, infection, hereditary, checkpoint inhibitors, immunosuppressants, and immunomodulators have been previously described. We describe an extremely rare case of a patient with marginal cell lymphoma treated with bendamustine, rituximab, and obinutuzumab presenting with severe, relapsing, chronic diarrhea with persistent apoptotic enteropathy of unknown etiology, despite hematological remission and discontinuation of treatment for 1 year.

Cerebral Venous Thrombosis in Young Adults: Unknown Origins.

Cerebral venous thrombosis (CVT) is a relatively rare neurological disorder that may result in significant morbidity if not diagnosed and managed promptly. The clinical presentation of CVT is nonspecific and highly variable with acute, subacute, or chronic onset. It most often presents as a headache but may present with focal neurological symptoms, symptoms of intracranial hypertension, or encephalopathy. The predisposing factors for CVT are mainly acquired and genetic hypercoagulable conditions. However, the epidemiology, predisposing factors, and clinical presentation of CVT are not clearly established given the rare nature of the condition. We present a case series of three young patients who did not have any classic underlying etiology for CVT or any prior diagnosis of venous thrombosis. We want to report this case series to show that a high index of suspicion should be maintained regardless of the absence of risk factors.

Cerebral Venous Thrombosis in a Patient With Smith-Magenis Syndrome.

Smith-Magenis syndrome (SMS) is a complex genetic developmental disorder characterized by distinctive physical features, cognitive impairment, developmental delay, and behavioral abnormalities. It is caused by a microdeletion of chromosome band 17p11.2 encoding for multiple genes including the Retinoic-acid-induced (RAI1) gene. RAI1 gene is expressed in many tissues, acting as a transcriptional regulator. It is a dosage-sensitive gene. The variants of the RAI1 gene have been explored with some contributing to systemic manifestations. The hematological manifestations such as venous thrombosis (VT) including cerebral venous thrombosis (CVT) have not been reported to date. We report a case of a 25-year-old female with SMS who presented with lethargy and gastrointestinal symptoms and was diagnosed with CVT. Our case highlights the risk of VT in patients with SMS and therefore holding a high index of suspicion for early diagnosis and management.

Anticancer Activity of Novel Difluorinated Curcumin Analog and Its Inclusion Complex with 2-Hydroxypropyl-ß-Cyclodextrin against Pancreatic Cancer.

Pancreatic ductal adenocarcinoma (PDAC) is the primary reason for cancer-related deaths in the US. Genetic mutations, drug resistance, the involvement of multiple signaling pathways, cancer stem cells (CSCs), and desmoplastic stroma, which hinders drug penetrance, contribute to poor chemotherapeutic efficacy. Hence, there is a need to identify novel drugs with improved delivery to improve treatment outcomes. Curcumin is one such compound that can inhibit multiple signaling pathways and CSCs. However, curcumin's clinical applicability for treating PDAC is limited because of its poor solubility in water and metabolic instability. Hence, we developed a difluorinated curcumin (CDF) analog that accumulates selectively in the pancreas and inhibits PDAC growth in vitro and in vivo. In the present work, we developed its 2-hydroxy-propyl-ß-cyclodextrin (HCD) inclusion complex to increase its water solubility and hydrolytic stability. The CDFHCD inclusion complex was characterized by spectroscopic, thermal, and microscopic techniques. The inclusion complex exhibited increased aqueous solubility, hydrolytic stability, and antiproliferative activity compared to parent CDF. Moreover, CDF and CDFHCD inhibited colony and spheroid formation, and induced cell cycle and apoptosis in PDAC cell lines. Hence, CDFHCD self-assembly is an efficient approach to increase water solubility and anticancer therapeutic efficacy, which now warrants advancement towards a clinical proof of concept in PDAC patients.

Integrative Clinical and Genomic Characterization of MTAP-deficient Metastatic Urothelial Cancer.

Deficiency of MTAP (MTAPdef) mainly occurs because of homozygous loss of chromosome 9p21, which is the most common copy-number loss in metastatic urothelial cancer (mUC). We characterized the clinical and genomic features of MTAPdef mUC in 193 patients treated at MD Anderson Cancer Center (MDACC) and 298 patients from the phase 2 IMvigor210 trial, which investigated atezolizumab in cisplatin-ineligible and platinum-refractory disease. In the MDACC cohort, visceral metastases were significantly more common for MTAPdef (n = 48) than for MTAP-proficient (MTAPprof; n = 145) patients (75% vs 55.2%; p = 0.02). MTAPdef was associated with poor prognosis (median overall survival [mOS] 12.3 vs 20.2 mo; p = 0.007) with an adjusted hazard ratio of 1.93 (95% confidence interval 1.35-2.98). Similarly, IMvigor210 patients with MTAPlo (n = 29) had a higher incidence of visceral metastases than those with MTAPhi tumors (n = 269; 86.2% vs 72.5%; p = 0.021) and worse prognosis (mOS 8.0 vs 11.3 mo; p = 0.042). Hyperplasia-associated genes were more frequently mutated in MTAPdef tumors (FGFR3: 31% vs 8%; PI3KCA: 31% vs 19%), while alterations in dysplasia-associated genes were less common in MTAPdef tumors (TP53: 41% vs 67%; RB1: 0% vs 16%). Our findings support a distinct biology in MTAPdef mUC that is associated with early visceral disease and worse prognosis. PATIENT SUMMARY: We investigated the outcomes for patients with the most common gene loss (MTAP gene) in metastatic cancer of the urinary tract. We found that this loss correlates with worse prognosis and a higher risk of metastasis in internal organs. There seems to be distinct tumor biology for urinary tract cancer with MTAP gene loss and this could be a potential target for treatment.

Spectrum of Histologic Findings in Orchiectomy Specimens of Patients Seeking Male to Female Physical Adaptation.

Context. Hormonal therapy followed by orchiectomy is of the standard of care in management of gender identity disorder in patients seeking male to female transition. The orchiectomy specimens from these patients are routinely subjected to histopathologic evaluation. We discuss the spectrum of histopathologic findings, incidental findings, and cost analysis of processing these specimens. Design. Orchiectomy specimens from patients seeking male to female transition received at our institution from January 2019 to June 2021 were included in the study. Data including patient age, history of hormonal therapy, testicular weight, histopathologic findings, number of tissue sections, and processing cost were collected. Results. A total of 79 specimens were identified. Mean patient age was 36.7 ± 14.5 years. Mean testicular weight was 28.0 ± 8.3 g (right) and 27.8 ± 9.1 g (left). Histologic evaluation showed diminished or absent spermatogenesis in 100% and fibrosis of seminiferous tubules in 96% of specimens. Benign, incidental findings, none of which altered patient management were present in 6 specimens (8%). For most specimens, 3 sections per testis were submitted. This resulted in a mean of 5.8 ± 1.1 tissue sections submitted per specimen. Conclusions. Orchiectomy specimens from patients with gender dysphoria always demonstrate hormone-therapy effects albeit with varying degree. The chances of discovering any incidental finding of clinical significance are negligible. Diligent gross inspection and minimal tissue sampling with additional sampling reserved for gross abnormalities can adequately document the histologic findings in a cost-effective manner.

Automated deep bottleneck residual 82-layered architecture with Bayesian optimization for the classification of brain and common maternal fetal ultrasound planes.

Despite a worldwide decline in maternal mortality over the past two decades, a significant gap persists between low- and high-income countries, with 94% of maternal mortality concentrated in low and middle-income nations. Ultrasound serves as a prevalent diagnostic tool in prenatal care for monitoring fetal growth and development. Nevertheless, acquiring standard fetal ultrasound planes with accurate anatomical structures proves challenging and time-intensive, even for skilled sonographers. Therefore, for determining common maternal fetuses from ultrasound images, an automated computer-aided diagnostic (CAD) system is required. A new residual bottleneck mechanism-based deep learning architecture has been proposed that includes 82 layers deep. The proposed architecture has added three residual blocks, each including two highway paths and one skip connection. In addition, a convolutional layer has been added of size 3 × 3 before each residual block. In the training process, several hyper parameters have been initialized using Bayesian optimization (BO) rather than manual initialization. Deep features are extracted from the average pooling layer and performed the classification. In the classification process, an increase occurred in the computational time; therefore, we proposed an improved search-based moth flame optimization algorithm for optimal feature selection. The data is then classified using neural network classifiers based on the selected features. The experimental phase involved the analysis of ultrasound images, specifically focusing on fetal brain and common maternal fetal images. The proposed method achieved 78.5% and 79.4% accuracy for brain fetal planes and common maternal fetal planes. Comparison with several pre-trained neural nets and state-of-the-art (SOTA) optimization algorithms shows improved accuracy.

Chronic Myeloid Leukaemia In A 14-Year-Old Child: An Unusual Case Report.

Chronic myeloid leukaemia is a myeloproliferative stem cell disorder characterized by dysregulated production and proliferation of myeloid cells. Hallmark of the disease is the reciprocal translocation between chromosome 9 and 22 giving rise to an abnormal chromosome known as Philadelphia chromosome. Approximately 15% of all leukaemias are chronic myeloid leukaemia with a slight male predominance. This is a case of a 14-year-old boy with no premorbid presented with 4 months history of fatigue and shortness of breath on exertion. On examination patient was vitally stable. On general physical examination there was pallor and sternal tenderness. On abdominal examination spleen was palpable with a size of about 8cm. Respiratory, cardiovascular and musculoskeletal examination was unremarkable. Complete blood picture showed leukocytosis, low haemoglobin and normal platelets. Erythrocyte sedimentation rate was 65 mm/hr. Liver function tests, Renal function tests, Serum electrolytes, Urine routine examination and c-reactive protein were normal. Chest x-ray and Electrocardiography was normal. Peripheral blood smear showed neutrophils 56% with 3% lymphocytes, 1% blasts and retic count of 0.5%. Bone marrow biopsy was suggestive of chronic myeloid leukaemia which was confirmed by FISH and Cytogenetic studies.

COVID-19 classification using chest X-ray images based on fusion-assisted deep Bayesian optimization and Grad-CAM visualization.

The COVID-19 virus's rapid global spread has caused millions of illnesses and deaths. As a result, it has disastrous consequences for people's lives, public health, and the global economy. Clinical studies have revealed a link between the severity of COVID-19 cases and the amount of virus present in infected people's lungs. Imaging techniques such as computed tomography (CT) and chest x-rays can detect COVID-19 (CXR). Manual inspection of these images is a difficult process, so computerized techniques are widely used. Deep convolutional neural networks (DCNNs) are a type of machine learning that is frequently used in computer vision applications, particularly in medical imaging, to detect and classify infected regions. These techniques can assist medical personnel in the detection of patients with COVID-19. In this article, a Bayesian optimized DCNN and explainable AI-based framework is proposed for the classification of COVID-19 from the chest X-ray images. The proposed method starts with a multi-filter contrast enhancement technique that increases the visibility of the infected part. Two pre-trained deep models, namely, EfficientNet-B0 and MobileNet-V2, are fine-tuned according to the target classes and then trained by employing Bayesian optimization (BO). Through BO, hyperparameters have been selected instead of static initialization. Features are extracted from the trained model and fused using a slicing-based serial fusion approach. The fused features are classified using machine learning classifiers for the final classification. Moreover, visualization is performed using a Grad-CAM that highlights the infected part in the image. Three publically available COVID-19 datasets are used for the experimental process to obtain improved accuracies of 98.8, 97.9, and 99.4%, respectively.

Juxtaglomerular cell tumor: report of a case with unusual presentation.

Juxtaglomerular cell tumor is a benign, renin-secreting neoplasm. The tumor arises from the juxtaglomerular apparatus cells of the kidney. Because the tumor is hormonally active, patients usually suffer from hypokalemia, hyperaldosteronism, and hypertension. Herein, we describe a case of a 19-year-old Asian female with a somewhat unusual presentation. A 19-year-old Asian female presented with upper extremity weakness, numbness, and tingling. On physical examination, the only notable finding was hypertension. Extensive workup revealed elevated aldosterone level and plasma renin activity. CT scan of the abdomen revealed a 2.2 cm mass in the lower pole of the left kidney. The mass was resected by partial nephrectomy. On microscopic evaluation, the tumor had glomoid appearance with sheets of uniform, round to polygonal cells with clear to eosinophilic cytoplasm. Immunohistochemical stains showed the tumor cells to be positive for CD117, CD34 and CD10 and negative for ER, PR, CK7, PAX-8, pan-cytokeratin, EMA, S100, Melan-A, HMB45, SMA and CAIX. Diagnosis of Juxtaglomerular cell tumor was rendered. This case highlights the importance of a regular physical exam and a high index of suspicion in patients presenting with unusual complaints.

Is a Preservation Solution for Living Donor Liver Transplantation Needed? Adding a New Chapter in LDLT!

Preservation solutions are required for organ viability in deceased donor liver transplantation (LT). However, their role in live donor LT (LDLT) has not been standardized. Methods: Eighty adult recipients who underwent right lobe LDLT at the Department of Liver Transplantation Surgery, Gambat, Pakistan, were studied. Based on shorter cold ischemia time and no back table reconstruction work, recipients were assigned to receive "no preservation solution" (cases/non-histidine-tryptophan-ketoglutarate group; n = 40) or "HTK group" (controls; n = 40). Early allograft dysfunction (bilirubin, transaminases, and international normalized ratio), postoperative complications (biliary and vascular), hospital stay, and 1-y survival were reported. The direct cost was also reported. Results: Demographics and clinical characteristics were comparable in the 2 groups. Comparing cases versus controls, mean bilirubin, alanine aminotransferase, aspartate aminotransferase, and international normalized ratio on postoperative day 7 were similar in the 2 groups. Five (12.5%) cases and 4 (10%) controls developed early allograft dysfunction (P = 0.72). Post-LT complications (biliary leak 2.5% in cases versus 0 in control), strictures (15% in cases versus 17.5% in controls), hepatic artery thrombosis (2.5% versus 00%)' and portal vein thrombosis (0 versus 2.5%) were comparable. Mean hospital stay (10.80 + 2.36 and 11.78 + 2.91 d) and 30 d mortality (2.5% versus 5%) were also comparable. Finally, 1-y survival based on Kaplan-Meier analysis was comparable in both groups (ie, 92.5%; non-HTK group versus 90%; HTK group) (P = 0.71). The direct cost of using a non-HTK-based approach was less than the HTK solution. Conclusion: In a selected cohort of right lobe LDLT recipients, preservation solutions can be avoided safely with comparable outcomes.

Calcifying fibrous tumor.

Calcifying fibrous tumor is a rare benign mesenchymal neoplasm. The etiology and pathogenesis of this tumor are uncertain. It has wide anatomical distribution. The tumor is most commonly found in the soft tissues of the extremities in younger individuals. However, in middle-aged patients, it tends to affect the visceral locations more commonly. In visceral location, it can mimic aggressive lesions clinically. The purpose of this report is to describe a case of calcifying fibrous tumor in a 71-year-old female with a history of breast carcinoma who was found to have an incidental small bowel mass on her follow-up. Clinically and radiologically, the mass was suspicious for either metastatic disease or gastrointestinal stromal tumor. The patient underwent open small bowel resection, and a 6.5 cm segment of the small bowel was sent to pathology. Grossly, a 2.0 cm tan-pink smooth round submucosal polyploid mass protruding into the lumen, mimicking a gastrointestinal stromal tumor, was identified. The tumor was hard and serially sectioned to reveal a white, calcified cut surface. Microscopically, the tumor appeared hypocellular and composed of scant spindle cells embedded in a dense, hyalinized and calcified collagenous stroma. Immunohistochemical stains for pan-cytokeratin, DOG1, desmin, S100, CD34, and MUC4 were negative, and a diagnosis of the calcifying fibrous tumor was rendered. This case provides a rare gross specimen image of calcifying fibrous tumor and highlights the importance of knowledge of rare entities in providing an accurate diagnosis for entities that can mimic other lesions.

COVID-19 classification using chest X-ray images: A framework of CNN-LSTM and improved max value moth flame optimization.

Coronavirus disease 2019 (COVID-19) is a highly contagious disease that has claimed the lives of millions of people worldwide in the last 2 years. Because of the disease's rapid spread, it is critical to diagnose it at an early stage in order to reduce the rate of spread. The images of the lungs are used to diagnose this infection. In the last 2 years, many studies have been introduced to help with the diagnosis of COVID-19 from chest X-Ray images. Because all researchers are looking for a quick method to diagnose this virus, deep learning-based computer controlled techniques are more suitable as a second opinion for radiologists. In this article, we look at the issue of multisource fusion and redundant features. We proposed a CNN-LSTM and improved max value features optimization framework for COVID-19 classification to address these issues. The original images are acquired and the contrast is increased using a combination of filtering algorithms in the proposed architecture. The dataset is then augmented to increase its size, which is then used to train two deep learning networks called Modified EfficientNet B0 and CNN-LSTM. Both networks are built from scratch and extract information from the deep layers. Following the extraction of features, the serial based maximum value fusion technique is proposed to combine the best information of both deep models. However, a few redundant information is also noted; therefore, an improved max value based moth flame optimization algorithm is proposed. Through this algorithm, the best features are selected and finally classified through machine learning classifiers. The experimental process was conducted on three publically available datasets and achieved improved accuracy than the existing techniques. Moreover, the classifiers based comparison is also conducted and the cubic support vector machine gives better accuracy.

Role of antibiotic prophylaxis on surgical site infection prevention in a low-risk population undergoing laparoscopic cholecystectomy: A randomized controlled study.

Objective: To compare the incidence of surgical site infections (SSIs) in low-risk patients undergoing laparoscopic cholecystectomy (LC) with pre-operative antibiotics versus no pre-operative antibiotics administration. Study design: Randomized controlled study. Setting: Hepatobiliary department, Pir Abdul Qadir Shah Jeelani Institute of Medical Sciences, Pakistan, from Jul 1, 2018, to Jun 30, 2021. Methods: This is a prospective, open-label, randomized study. Individuals scheduled for laparoscopic cholecystectomy who met the inclusion requirements were randomly assigned to two groups. Group A patients received pre-operative antibiotics (intravenous cefazolin 2-g), and group B patients were operated on without administration of pre-operative antibiotics. Post-operatively, patients were studied for the occurrence of SSIs for 30 days. Results: The mean age of patients in group A was 40.6 + 5.2 years, while group B was 41.04 + 5.03. The male to female ratio was 1:3. Gender distribution showed female dominance in both groups, i.e., 78.74% in group A and 76.80% in group B. The incidence of SSI in group A was 3.98%, while in group B was 4.9% (p-value = 0.584). No statistical significance was found while comparing both groups' age, gender, operative duration, and hospital stay. Conclusion: This study showed comparable results between both groups, and prophylactic antibiotics have no impact in preventing SSIs. In low-risk individuals undergoing laparoscopic cholecystectomy, the incidence of SSIs is quite low, and prophylactic antibiotics can be avoided.

Breast Cancer Classification from Ultrasound Images Using Probability-Based Optimal Deep Learning Feature Fusion.

After lung cancer, breast cancer is the second leading cause of death in women. If breast cancer is detected early, mortality rates in women can be reduced. Because manual breast cancer diagnosis takes a long time, an automated system is required for early cancer detection. This paper proposes a new framework for breast cancer classification from ultrasound images that employs deep learning and the fusion of the best selected features. The proposed framework is divided into five major steps: (i) data augmentation is performed to increase the size of the original dataset for better learning of Convolutional Neural Network (CNN) models; (ii) a pre-trained DarkNet-53 model is considered and the output layer is modified based on the augmented dataset classes; (iii) the modified model is trained using transfer learning and features are extracted from the global average pooling layer; (iv) the best features are selected using two improved optimization algorithms known as reformed differential evaluation (RDE) and reformed gray wolf (RGW); and (v) the best selected features are fused using a new probability-based serial approach and classified using machine learning algorithms. The experiment was conducted on an augmented Breast Ultrasound Images (BUSI) dataset, and the best accuracy was 99.1%. When compared with recent techniques, the proposed framework outperforms them.

D2BOF-COVIDNet: A Framework of Deep Bayesian Optimization and Fusion-Assisted Optimal Deep Features for COVID-19 Classification Using Chest X-ray and MRI Scans.

BACKGROUND AND OBJECTIVE: In 2019, a corona virus disease (COVID-19) was detected in China that affected millions of people around the world. On 11 March 2020, the WHO declared this disease a pandemic. Currently, more than 200 countries in the world have been affected by this disease. The manual diagnosis of this disease using chest X-ray (CXR) images and magnetic resonance imaging (MRI) is time consuming and always requires an expert person; therefore, researchers introduced several computerized techniques using computer vision methods. The recent computerized techniques face some challenges, such as low contrast CTX images, the manual initialization of hyperparameters, and redundant features that mislead the classification accuracy. METHODS: In this paper, we proposed a novel framework for COVID-19 classification using deep Bayesian optimization and improved canonical correlation analysis (ICCA). In this proposed framework, we initially performed data augmentation for better training of the selected deep models. After that, two pre-trained deep models were employed (ResNet50 and InceptionV3) and trained using transfer learning. The hyperparameters of both models were initialized through Bayesian optimization. Both trained models were utilized for feature extractions and fused using an ICCA-based approach. The fused features were further optimized using an improved tree growth optimization algorithm that finally was classified using a neural network classifier. RESULTS: The experimental process was conducted on five publically available datasets and achieved an accuracy of 99.6, 98.5, 99.9, 99.5, and 100%. CONCLUSION: The comparison with recent methods and t-test-based analysis showed the significance of this proposed framework.