diff --git a/.DS_Store b/.DS_Store
new file mode 100644
index 0000000..87cc0b3
Binary files /dev/null and b/.DS_Store differ
diff --git a/frontend/app/about/page.tsx b/frontend/app/about/page.tsx
new file mode 100644
index 0000000..ef19d4e
--- /dev/null
+++ b/frontend/app/about/page.tsx
@@ -0,0 +1,82 @@
+import Austin from "../../assets/austin.png";
+import Shawn from "../../assets/shawn.jpg";
+import Brandon from "../../assets/brandon.jpeg";
+
+const test = () => {
+ return (
+
+
+
The Model
+
+ This neural network model is based on EfficientNetB3, comprising
+ 11,184,179 parameters (approximately 42.66 MB). It's designed for
+ image classification with 256x256x3 input images. The model includes a
+ Batch Normalization layer, a Dense layer with 256 neurons, dropout for
+ regularization, and an output layer with 4 classes. While leveraging a
+ pre-trained model like EfficientNetB3 boosts performance, it presents
+ challenges such as high parameter count, fine-tuning complexities,
+ memory/storage demands, and the need for substantial training data.
+ These aspects should be considered when using this model in practice.
+ Still we were able to reach an accuracy of 0.9925 on our testing set.
+ We got our dataset from
+
+ {" "}
+ Kaggle
+
+ . We used the
+
+ {" "}
+ Effecient Net V3.
+
+
{routes.map((route, index) => (
-
+
{route.title}
))}
diff --git a/frontend/app/page.tsx b/frontend/app/page.tsx
index 31a4101..36e63e2 100644
--- a/frontend/app/page.tsx
+++ b/frontend/app/page.tsx
@@ -1,9 +1,28 @@
-import Image from 'next/image'
+import Image from "next/image";
+import Mri from "../assets/mri.png"
export default function Home() {
return (
-
-
test home
+
+
+
+
+
TumorFinders
+
+ Exploring AI Precision in Brain Tumor Detection
+
+
+ At TumorFinders, we delve deep into AI precision. Our app is a
+ playground for testing and optimizing custom AI models for brain
+ tumor identification from MRI scans. Join us on a journey through
+ the technical frontier of medical AI at TumorFinders.
+